Doctor of Philosophy, The Ohio State University, 2023, Educational Studies

As mobile learning (m-learning) becomes increasingly prevalent in education, it is recognized for its potential to enhance the overall quality of teaching and learning. Despite the many benefits, m-learning apps often experience low retention rates, which directly impede learners' benefit from using them and cause a waste of resources in app design, development, and maintenance. To investigate the critical factors influencing learners' acceptance of m-learning outside the classroom, this study introduced a novel model, the Mobile Learning Acceptance Determination (mLAD) Model, based on the Technology Acceptance Model and the updated DeLone and McLean Information System Success Model. Through the mLAD model, the study identified the critical app quality factors that influence learners' acceptance of m-learning. The moderating effects of the type of m-learning apps on learners' acceptance of m-learning were also revealed. An online questionnaire named the m-Learning Acceptance Questionnaire (mLAQ) was developed and disseminated through Amazon Mechanical Turk. A total of seven hundred forty-seven adult learners in the U.S. participated in the study. The descriptive statistical results of the examined factors revealed that m-learning apps available in the market demonstrate high mobility and content quality. Still, their interactivity and service quality could be improved. Furthermore, the results of the structural equation modeling analysis indicated that learners' two beliefs, perceived usefulness, and perceived ease of use, are the two essential determinants of learners' intention to use m-learning apps outside the classroom. Quality factors, such as content quality, interface design, mobility, and service quality, are the antecedents of learners' m-learning acceptance, given that they significantly and directly influence perceived usefulness and ease of use and indirectly impact learners' intention to use m-learning apps through learners' two beliefs. Through (open full item for complete abstract)

Committee: Ana-Paula Correia (Advisor); Minjung Kim (Committee Member); Richard J Voithofer (Committee Member) Subjects: Education; Educational Software; Educational Technology; Information Systems; Information Technology; Technology

Master of Science, Miami University, 2020, Mechanical and Manufacturing Engineering

Vibration based monitoring techniques are widely used to detect damage, monitor the growth of inherent defects, system identification, and material parameter estimation for various engineering applications. These techniques present a non-invasive and relatively inexpensive tool for various biomedical applications, for example, in characterizing the mechanical properties of the bone and muscles of humans as well as animals. In recent years, it has been shown that fundamental natural frequencies and corresponding damping ratios can be correlated to the bone health quality indicators as associated with osteoporosis, osteoarthritis etc. In this research, through the investigation of clinical data, an analysis procedure is developed to investigate the correlation between the damping properties associated with both lower and higher modes of vibration and bone health quality. Subsequently, a data-driven system identification tool for reconstructing the parameters (mass, stiffness, damping distributions) in a low-dimensional human model is developed which utilizes selected measurements from the clinical study. It is anticipated that the analysis process and parameter identification techniques presented here can be developed and tuned for any individual human model and can be can be used as osteological monitoring tool for predicting early diagnostics pre-cursors of the bone or muscle related conditions or diseases.

Committee: Kumar Singh (Advisor); James Chagdes (Committee Member); Mark Walsh (Committee Member) Subjects: Biomechanics; Biomedical Engineering; Mechanical Engineering; Osteopathic Medicine

Master of Arts (MA), Bowling Green State University, 2015, Psychology/Clinical

Objective. Social capital is generally defined as an individual's potential for tangible or social resources made available via interpersonal connections. Higher levels are related to a variety of positive health, well-being, and occupational outcomes. Social capital can be measured by a variety of indices in adulthood. Currently, the childhood precursors to adult social capital are relatively unknown. The current project tests a developmental-contextual model for both the measurement of social capital in adulthood and the childhood precursors that may impact its accumulation over the life course. Methods. 523 participants were surveyed at age 8 and again at age 48 as part of a 40-year prospective, longitudinal study. Participants completed measures of cognitive abilities, relationships with parents, peers, and spouses, information about personal traits, and beliefs and attitudes regarding social relationships. Structural Equation Modeling (SEM) was used to test the hypothesized model. Results. The proposed measurement model of social capital was generally supported, with latent domains of individual, interpersonal, socio-economic, and community level indices. Predictive models of childhood precursors for social capital differed by gender: prominent precursors were childhood aggression and cognitive ability for males, and childhood family religiosity for females. Conclusions. These findings suggest that a developmental-contextual model of social capital may account for the multiple indices of social capital and that its accumulation across the life course is different for males and females. Knowledge of precursors may be clinically helpful for fostering social capital growth in therapeutic settings.

Committee: Eric Dubow Ph.D. (Advisor); Carolyn Tompsett Ph.D. (Committee Member); Marie Tisak Ph.D. (Committee Member) Subjects: Clinical Psychology

Doctor of Philosophy, The Ohio State University, 2015, Agricultural, Environmental and Developmental Economics

Over the past few years, U.S. agriculture has experienced a myriad of macroeconomic and environmental changes that have profound implications for the well-being of farm households and the farm sector. An expanding biofuels market and growing export demand from China have led to rising agricultural commodity prices since mid-2000s. However, during the same time period, the residential housing market collapsed in 2007-2008 could impose a downturn pressure on farmland market, and there is a growing concern for environmental problems due to excessive agricultural nutrient runoff as well as stronger calls for more effective agri-environmental policies to curb nonpoint source agricultural pollution. Economic analyses of farmer decisions in this constrained and evolving environment are critical to understand how these changes have impacted farmer welfare and trade-offs with ecosystem and other societal benefits. Using individual-level data on farmland parcels and farmers from Ohio and Lake Erie basin, my dissertation examines how the recent housing market bust, expanding ethanol production, and rising environmental concerns have impacted farmers' land use, land management, and land transaction decisions and the implications for farmer welfare. Farm real estate represents over 80% of the balance sheet of the farm sector and is the single largest item in a typical farmer's investment portfolio, and thus changes in farmland values could affect the welfare of the farmer household and farm sector in general. The first two chapters of my dissertation examine the trends and determinants of farmland values in the Midwest in the 2000s decade. In particular, the first chapter identifies the impact of the recent residential housing market bust and subsequent economic recession on farmland values, using parcel-level farmland sales data from 2001-2010 for a 50-county region under urbanization pressure in Western Ohio. My estimates from hedonic regressions reveal that farmland was no (open full item for complete abstract)

Committee: Elena Irwin (Advisor); Brian Roe (Committee Member); Sathya Gopalakrishnan (Committee Member) Subjects: Agricultural Economics; Agriculture; Economics; Environmental Economics; Public Policy; Sustainability; Water Resource Management

Doctor of Philosophy, The Ohio State University, 2014, EDU Policy and Leadership

Under the framework of structural equation modeling (SEM), longitudinal data can be analyzed using latent growth models (LGM). An extension of the simple LGM is the multilevel latent growth model, which can be used to fit clustered data. The purpose of this study is to investigate the performance of five different missing data treatments (MDTs) for handling missingness due to longitudinal attrition in a multilevel LGM. The MDTs are: (1) listwise deletion (LD), (2) FIML, (3) EM imputation, (4) multiple imputation based on regression (MI-Reg), and (5) MI based on predictive mean matching (MI-PMM). A Monte Carlo simulation study was conducted to explore the research questions. First, population parameter values for the model were estimated from a nationally representative sample of elementary school students. Datasets were then simulated based on a two-level LGM, with different growth trajectories (constant, decelerating, accelerating), and at varying levels of sample size (200, 500, 2000,10000). After datasets are generated, a designated proportion of data points (5%, 10%, 20%) were deleted based on different mechanism of missingness (MAR, MNAR), and the five missing data treatments were applied. Finally, the parameter estimates produced by each missing data treatment were compared to the true population parameter values and to each other, according to the four evaluation criteria: parameter estimate bias, root mean square error, length of 95% confidence intervals (CI), and coverage rate of 95% CIs. Among the five MDTs studied, FIML is the only MDT that yields satisfactory bias level as well as coverage rate for all parameters across all sample sizes, attrition rates, and growth trajectories under MAR. It is also the only MDT that consistently outperforms the conventional MDT, LD, in every aspect, especially when missingness ratio increases. Under MNAR, however, estimates of the predictor effects on slopes become biased and coverage for those two paramet (open full item for complete abstract)

Committee: Richard Lomax (Advisor); Paul Gugiu (Committee Member); Eloise Kaizar (Committee Member) Subjects: Education; Statistics

PhD, University of Cincinnati, 0, Allied Health Sciences: Communication Sciences and Disorders

Purpose: This study investigated the differences in brain functional activation patterns and in white matter microstructure integrity underlying atypical left ear advantage (LEA) for speech-related stimuli in dichotic listening. While a finding of atypical LEA for speech-related stimuli is often taken as an indication of mixed/reversed hemispheric language dominance and as a marker for auditory processing disorder (APD), validation studies using gold standard techniques failed to predict right hemispheric dominance from LEA. Furthermore, the interpretation made by clinical audiologists has never been tested using objective techniques of evoked potentials or imaging studies It is indeterminate whether a sensory processing deficit such as APD or other supramodal factors may underlie the finding of atypical LEA. Design: Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data were acquired from a cohort of 12 children with auditory processing complaints, manifesting an atypical LEA and 12 typically developing children with right ear advantage (REA), aged 7 to 14 years. Significant clusters showing between-group activation differences (fMRI) and diffusivity measures (i.e., fractional anisotropy, mean diffusivity, radial and axial diffusivity) were computed. Results: fMRI results indicated that differences in brain activation patterns were due to attenuated deactivation of the default mode network (DMN) and increased activation in the dorsal anterior cingulate cortex (dACC) in the LEA compared to the REA group. DTI results indicated altered frontal white matter microstructure, reflected by decreased fractional anisotropy in frontal multifocal white matter regions, specifically in prefrontal cortex bilaterally and left anterior cingulate white matter. Furthermore, results revealed increased mean diffusivity in the left sublenticular part of the internal capsule in the LEA compared to the REA group. Conclusion: Collectively, our findin (open full item for complete abstract)

Committee: Robert Keith Ph.D. (Committee Chair); Vincent Schmithorst Ph.D. (Committee Member); Scott Holland Ph.D. (Committee Member); Fawen Zhang Ph.D. (Committee Member) Subjects: Audiology

Doctor of Philosophy (PhD), Wright State University, 2012, Human Factors and Industrial/Organizational Psychology PhD

There were four purposes for this study. One purpose was to develop a direct assessment of individuals' ethical schemas for how to operate within work settings. I proposed structural assessment using Pathfinder as a means of measuring the structural network of ethical knowledge. I expected structural assessment to be a better means of assessing moral development within organizations than the Defining Issues Test (Rest, 1979). A second purpose was to examine the extent to which implicit aspects of the ethical decision-making process have differential effects on behavioral criteria than explicit aspects of ethical decision-making. A third purpose of this study was to examine the impact that contextual factors (i.e., ethical work climates) have on ethical decision-making and behavior. Thus, I investigated the unique and interactive effects of ethical work climates and ethical decision-making on behavioral criteria. Finally, a fourth purpose of this study was to merge the ethical behavior and counterproductive work behavior (CWB) literatures to increase our understanding about theory and variables in both literatures. There were two data collections. In the first sample, I used college undergraduates to develop measures. In the second sample, I conducted formal tests of hypotheses. I recruited individuals who participated in Amazon's MechanicalTurk program, which reflected a diverse set of individuals with a wealth of work experience. In addition, I used full-time employees who were enrolled in an MBA program to increase sample size. I used hierarchical regression to test hypotheses. Results showed that using structural assessment and Pathfinder to measure ethical schemas accounted for unique variance in CWBs, controlling for the DIT, and that implicit processes exhibited a greater impact on CWBs than explicit processes. The implications for theory development, training, selection and organizational cultures are discussed.

Committee: Debra Steele-Johnson PhD (Committee Chair); Nathan Bowling PhD (Committee Member); Melissa Gruys PhD (Committee Member); David LaHuis PhD (Committee Member) Subjects: Behavioral Sciences; Cognitive Psychology; Ethics; Organizational Behavior; Psychology

PhD, University of Cincinnati, 2004, Arts and Sciences : Mathematics

In this dissertation, we use Bayesian methods to estimate parameters in measurement error models and in the two-period crossover trial. The reference prior approach is used to estimate parameters in the measurement error models, including simple normal structural models, Berkson models, structural models with replicates, and the hybrid models. Reference priors are derived. Jeffreys prior is obtained as a special case of reference priors. The posterior properties are studied. Simulation-based comparisons are made between the reference prior approach and the maximum likelihood method. A fractional Bayes factor (FBF) approach is used to estimate the treatment effect in the two-period crossover trial. The reference priors and the FBF are derived. The FBF is used to combine the carryover-effect model and the no-carryover-effect model. Markov chain Monte Carlo simulation is used to implement the Bayesian analysis.

Committee: Dr. Siva Sivaganesan (Advisor) Subjects: Mathematics; Statistics

PhD, University of Cincinnati, 2001, Medicine : Environmental Health Sciences

To achieve detailed understanding of an exposure-outcome association in public health studies, an investigator often needs to account for mediator(s). A mediator is a variable that occurs in a causal pathway from an independent to a dependent variable. The mediational model describes the associations among the exposure(s), mediator(s), and outcome(s). Statistics are needed to determine how much of the exposure-outcome association is due to a mediator. Although mediational models are widely applied in public health, sociological and psychological research, the statistical methods to define and test mediation effects are underdeveloped. The current available methods, path analysis and multi-step regression analyses, have some major limitations including: 1) lack of clear and meaningful definitions of mediation effects; 2) lack of significance testing procedures for the mediation effects; and 3) these methods have not been extended into a generalized form. The present study defined mediation effects, which allows for substantive accounting of the exposure-outcome process that is consistent across a class of generalized mediational models. The newly defined mediation effects have important epidemiological interpretations that are closely related to the concept of attributable risk (AR). Both linear and non-linear models were studied. Much attention has been given to the logistic mediational model due to its important role in the epidemiological studies. Asymptotic variance estimates for the mediation effects were derived using the multivariate delta method. Through Bayesian modeling and Monte Carol techniques, in particular, Markov chain Monte Carlo (MCMC), the posterior distributions for the mediation effects were estimated. Simulation studies, as well as case studies using a nationally representative database, compared the behavior of the asymptotic estimates and the non-informative Bayesian posterior estimates for the mediation effects of the linear and logistic medi (open full item for complete abstract)

Committee: Dr. Paul Succop (Advisor) Subjects: Health Sciences, General

Doctor of Philosophy, The Ohio State University, 2003, Psychology

This dissertation represents an investigation into the role of model complexity in structural equation modeling (SEM) and how traditional notions of model fit, which do not typically consider complexity, are inadequate for summarizing the success or failure of a model. Model fit is traditionally summarized in an index which communicates the agreement between a given model and a particular data set. However, demonstrating that a given model could have generated a given data set is not sufficient to show that it is a good model. Because complexity partially determines the ability of a model to fit data, model complexity should not be ignored when evaluating model fit. The importance of model complexity is examined in the SEM context by simulating correlation matrices and applying a series of models which differ in complexity. First, it is shown that models differing in the number of free parameters can fit the same random data differentially well by a simple criterion of good fit. Second, models with the same number of free parameters, but which differ in functional form, are found to fit the same data differentially well. Third, it is found that restrictions placed on parameter range have an impact on model complexity. Because traditional fit indices usually correct for model complexity due only to the number of free parameters, these findings have important implications for how models are assessed and compared in the SEM paradigm.

Committee: Robert MacCallum (Advisor) Subjects: Psychology, Psychometrics

Doctor of Philosophy (PhD), Ohio University, 2001, Physics (Arts and Sciences)

Until recently, structural models used to represent amorphous silicon (a-Si) in computer simulations were either perfectly fourfold connected random networks or random networks containing only miscoordinated atoms. These models are an approximation to the structure of the real material and do not uniformly comply with all the experimental data for a-Si. In this dissertation we make an attempt to go beyond this approximation and construct and examine models that have two major types of defects, encountered in real material, in their structure - nanovoids and crystalline grains. For our study of voids in a-Si we have calculated vibrational properties of structural models of a-Si with and without voids using ab initio and empirical molecular dynamics techniques. A small 216 atom and a large 4096 atom continuous random network (CRN) models for a-Si have been employed as starting points for our a-Si models with voids. Our calculations show that the presence of voids leads to an emergence of localized low-energy states in the vibrational spectrum of the model system. Moreover, it appears that these states are responsible for the anomalous behavior of system's specific heat at very low temperatures. To our knowledge these are the first numerical simulations that provide adequate agreement with experiment for the very low-temperature properties of specific heat in disordered materials within the limits of harmonic approximation. For our study of crystalline grains in a-Si we have developed a new procedure for the preparation of physically realistic models of paracrystalline silicon based on a modification of the bond-switching method of Wooten, Winer, and Weaire. Our models contain randomly oriented c-Si grains embedded in a disordered matrix. Our technique creates interfaces between the crystalline and disordered phases of Si with an extremely low concentration of coordination defects. The resulting models possess structural and vibrational properties comparable with those (open full item for complete abstract)

Committee: David Drabold (Advisor) Subjects: Physics, Condensed Matter

Master of Science, The Ohio State University, 2023, Mechanical Engineering

The National Aeronautics and Space Administration announcement indicating intent to return to the moon as a part of the Artemis plan has sparked research supporting the development of lunar surface technologies. Among these technologies are lunar power systems to provide energy for life support, lunar science experiments, and in-situ resource utilization. A candidate technology in this respect are direct current microgrids which are capable of grid reconfiguration and the integration of distributed energy resources and loads. There properties provide enhanced grid reliability in the face of system faults. However, existing power systems protections and diagnostics lack key detection and isolation capabilities when considering the wide range of faults that can occur in the direct current microgrid environment. This work seeks to develop a model-based fault diagnosis scheme for direct current microgrids using structural analysis methodologies. The proposed diagnostic concept includes three stages. First, traditional overcurrent protection for the rapid mitigation of low impedance short circuit faults. Second, sensor validation algorithms to detect sensor failure. Third, a model-based diagnostics concept leveraging analytical redundancy concepts to detect and isolate the remaining system faults. A structural microgrid model is created and applied for sensor placement analysis, detectability and isolability analysis, and residual generation. These residuals are used to design diagnostic tests to evaluate the presence of faults in the system. Microgrid models are developed in Simulink to generate calibration data for these diagnostic tests. A statistical microgrid model is developed to enable Monte Carlo analysis for the generation of validation data. The proposed diagnostic scheme is shown to provide fast fault detection while maintaining low error rates. It is demonstrated that model-based diagnostics offer the capability to detect and isolate a wider range of faults c (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Matilde D'Arpino (Advisor); Qadeer Ahmed (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2021, Materials Engineering

In this study, silver/epoxy conductive nanocomposite-based sensors were developed as follows: First, abundant silver nanomaterials were synthesized using a rapid polyol reduction method. Factors that affected silver nanomaterial morphology and the mechanism of nanosilver growth in large-scale synthesis were studied in detail. Controlling the silver nanomaterial's size and uniformity and efficiently purifying the silver nanowire were the main challenges in the development of large-scale synthesis. Second, the morphology, crystallinity, and orientation of various silver nanofillers were characterized. Then, silver nanoparticle/polyacrylonitrile and silver nanowire/polyacrylonitrile-based nanocomposites were fabricated by spin coating and used to investigate the silver nanocomposite conductive network. Silver nanowire-based nanocomposite showed a lower percolation threshold. A conductive unit-based model was established and successfully explained the evolution of the conductive network and aggregation. The aggregation geometry of nanofiller appeared as a dominant factor in altering the percolation behavior. Small-sized, irregularly shaped silver nanoparticle aggregates can lower the percolation threshold by introducing anisotropy to the nanocomposite. In contrast, large-sized, irregularly shaped silver nanoparticle aggregates hinder the formation of the conductive network due to the number of aggregates decreasing. Lastly, the silver conductive nanocomposite-based structural health monitoring sensors were designed to detect the progress of chemical diffusion and material degradation as a function of time. A comparison study between the silver nanowire/epoxy sensor and silver nanoparticle/epoxy sensor was conducted to investigate the concentration and geometry of the silver nanomaterial's effect on acid penetration. It appeared that the structural health monitoring sensors' resistance decreased in three stages as the diffusion time progressed. When the volume percenta (open full item for complete abstract)

Committee: Khalid Lafdi Ph.D. (Committee Chair); Donald Klosterman Ph.D. (Committee Member); Erick Vasquez Ph.D. (Committee Member); Youssef Raffoul Ph.D. (Committee Member) Subjects: Chemical Engineering; Materials Science

PHD, Kent State University, 2021, College of Education, Health and Human Services / School of Lifespan Development and Educational Sciences

The purpose of this study was to investigate the relationship among self-efficacy, self advocacy, and multicultural counseling competency of school counselors currently practicing in the US. The research questions guided this study included (a) What are the direct and indirect influences of school counselor self-efficacy on multicultural counseling competence? (b) Is the relationship between self-efficacy and multicultural counseling competence mediated by self-advocacy for school counselors? The data were collected from 306 school counselors practicing in the US. Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were used in the data analysis in the study. The results suggested self-efficacy significantly predicted multicultural counseling competence among the US school counselors. The results of the hypothesized structural model also suggested that self-advocacy had a strong indirect effect on multicultural counseling competence mediated by self-efficacy. The results of the data analysis, discussions of the findings, implications of the current study, and limitations and future research directions are presented herein.

Committee: Jason McGlothlin DR (Committee Chair); Martin Jencius DR (Committee Member); Kelly Cichy DR (Committee Member) Subjects: Counseling Education; School Counseling

Master of Science, The Ohio State University, 2021, Biomedical Engineering

The objective of this study was to modify a previously developed method of calculating stress and modeling stress-strain curves during dynamic frontal bending tests of human ribs and to evaluate the impact of the updates made to the model on the material properties extracted from the model. 14 whole mid-level ribs were experimentally tested and their stress calculations were updated to include a radius of curvature from fitting logarithmic spirals to the rib. The strain was transformed from one of two cutaneous strain gages to the fracture location of the rib. The updated stress and strain were run through the previously developed stress-strain curve model and the elastic modulus was extracted. When compared to the elastic modulus from tensile coupon testing in the literature, the structural elastic modulus was higher due to a higher stress and lower strain experienced in structural tests. Future work should focus on developing a transfer function for structural material properties and validation using subject specific finite element models of structural rib tests. Having an accurate method of extracting material properties of ribs from structural tests would provide a large database of material properties of human ribs for a wide range of subjects.

Committee: Amanda Agnew PhD (Advisor); Yun Seok Kang PhD (Advisor) Subjects: Biomechanics; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2020, Civil Engineering

Vibration-based structural health monitoring (SHM) has become increasingly popular in recent years as a general and global method to detect possible damage scenarios. With the increase in the number of infrastructures that are in service beyond their initial design service age, more and more owners are relying on SHM to evaluate the integrity of their structures. As a result, SHM approaches that are applicable to a variety of structures with minimal service interruption and lower cost are of high importance. There are many research on SHM processes using a network of sensors placed on over a target structure. Although these approaches may result in more accurate results due to redundancy of the system, they are mostly cost prohibitive for currently in-service structures and are suitable for newly constructed projects with embedded sensors. This dissertation presents a feature-based SHM process using a new signal processing and feature extraction methodology and presents its application on a real-life vibration monitoring project completed of an energized substation structure. The new signal processing and feature extraction methodology uses specific filtering and optimization schemes which improved the performance in extracting features specifically when using a contaminated response signal. Next, the extracted features are used in a structural model updating to identify and localize the damage through an optimization process. Finally, a vortex-induced vibration analysis process is presented and applied to the real-life monitored structure. Currently there are no power utility industry standard methodology for the analysis and design of structures against wind-induced vibrations. The current codes or standards of practice recommend using damping devices such as chain dampers or strakes to mitigate the vibrations, when they are observed. This approach may not be feasible due to the energized in-service structures. In addition, modifications to the installed structure (open full item for complete abstract)

Committee: Abdollah Shafieezadeh (Advisor); Jieun Hur (Committee Member); Halil Sezen (Committee Member) Subjects: Engineering

Doctor of Philosophy, The Ohio State University, 2020, Entomology

Urban areas are often considered adverse environments for wildlife communities given that the colonization and establishment of local species pools are disrupted by biotic and abiotic changes at different spatial scales such as the introduction of invasive species, periodic mowing, and changes in soil and air quality. Although the number of people residing in cities has increased in the last century, over 300 cities worldwide have shrunk due to prolonged economic decline and population loss, resulting in thousands of newly available greenspaces scattered throughout cities. Consequently, interest in urban greenspaces as sites for conservation has grown considerably, raising questions about the ability of these habitats to support wildlife. As novel ecosystems, urban areas represent a set of new challenges for local species pools, yet the mechanisms driving community assembly processes within cities is a major knowledge gap. My work focused on identifying species distributions, patterns, and processes leading to the successful establishment arthropods in cities. For this, I chose to work with beetle and spider assemblages as they are considered biological indicators of environmental changes at small and large spatial scales and are taxonomically and functionally diverse predatory groups. In Chapter 1, my objective was to determine how urban greenspaces management and design impacts Carabidae and Staphylinidae beetles using taxonomic and life-history trait approaches. I found that ecological and morphological traits were good indicators of how beetles were responding to greenspace management strategies. Most species were negatively associated with building structures, while undisturbed habitats supported more hygrophilous and brachypterous beetle populations. In Chapter 2, I investigated the importance of local and landscape characteristics on spider communities using taxonomic and functional diversity approaches. I found that Pardosa milvina (Lycosidae) was the mo (open full item for complete abstract)

Committee: Mary Gardiner PhD (Advisor); Luis Cañas PhD (Committee Member); Andrew Michel PhD (Committee Member); Robert Gates PhD (Committee Member); William Symondson PhD (Committee Member) Subjects: Agriculture; Animals; Behavioral Sciences; Biology; Biostatistics; Ecology; Entomology; Environmental Science; Forestry; Molecular Biology; Soil Sciences; Urban Forestry; Urban Planning; Wildlife Conservation; Wildlife Management

Doctor of Philosophy, The Ohio State University, 2020, Mechanical Engineering

Vehicle safety is one of the critical elements of modern automobile development. With increasing automation and complexity in safety-related electrical/electronic (E/E) systems, and given the functional safety standards adopted by the automotive industry, the evolution and introduction of electrified and automated vehicles had dramatically increased the need to guarantee unprecedented levels of safety and security in the automotive industry. The automotive industry has broadly and voluntarily adopted the functional safety standard ISO 26262 to address functional safety problems in the vehicle development process. A V-cycle software development process is a core element of this standard to ensure functional safety. This dissertation develops a model-based diagnostic methodology that is inspired by the ISO-26262 V-cycle to meet automotive functional safety requirements. Specifically, in the first phase, system requirements for diagnosis are determined by Hazard Analysis and Risk Assessment (HARA) and Failure Modes and Effect Analysis (FMEA). Following the development of system requirements, the second phase of the process is dedicated to modeling the physical subsystem and its fault modes. The implementation of these models using advanced simulation tools (MATLAB/Simulink and CarSim in this dissertation) permits quantification of the fault effects on system safety and performance. The next phase is dedicated to understanding the diagnosability of the system (given a sensor set), or the selection of a suitable sensor set to achieve the desired degree of diagnosability, using a graph-theoretic method known as structural analysis. By representing a system in directed-graph or incidence-matrix form, structural analysis allows the determination of analytical redundancy in the system and of the detectability and isolability of individual faults. Further, it provides a logical computation sequence for solving for system unknowns, by identifying analytical redundant relat (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Manoj Srinivasan (Committee Member); Ran Dai (Committee Member); Qadeer Ahmed (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Mechanical Engineering

Doctor of Philosophy (PhD), Ohio University, 2020, Educational Research and Evaluation (Education)

Two main research problems were addressed in the current study. First, the researcher explored the impact of e-mail prenotification, follow-up reminders and of mixed-mode design on survey response rates in Saudi Arabia among four conditions when applying a 2 (pre-notification: Yes, No) × 2 (Follow-up: E-mail, WhatsApp) between-subjects factorial design. Further, this study investigated the impact of including the phrase, “all I need is 10 more people,” during survey distribution. Results indicated that using both e-mail prenotification and follow-up reminders simultaneously, as well as multiple follow-up reminders in the form of both email and social media applications increased response rate. Further, using the phrase “all I need is 10 more people” during the second-follow reminder both elevated the response rate and provided support from the university to the researcher. Second, the researcher demonstrated whether the analytic choice between MR and SEM affected the results when examining the factors impacting the research productivity of faculty members in Saudi Arabia. Results indicated that using either MR or SEM delivered different results in terms of significant predictors and the model's overall explained variance. Further, differential outcomes produced by the various SEM models employed illustrate how the incorrection specification of formative (causal) indicators can result in worse data-fitting models. Implications for selecting analytic procedures are discussed.

Committee: Gordon Brooks (Committee Chair); Yuchun Zhou (Committee Member); Charles Lowery (Committee Member); Lijing Yang (Committee Member); Anirudh Ruhil (Committee Member) Subjects: Education

Doctor of Philosophy, The Ohio State University, 2019, Mechanical Engineering

Technology and societal advancements drive industry adaptations. What was considered advanced ten years ago is now considered traditional, and what is advanced today will become the new standard. Systems in the modern world are increasing in complexity and requiring a broader range of components to provide the functions necessary to stay competitive and operate at peak potential. Technological advancements and the demand for immediate satisfaction only fuel the progression. What must occur in the background for the modern world to operate efficiently and with as few problems and delays as possible? How do large, complex systems operate without issues? If an issue were to occur, how can that issue be detected, isolated, and resolved with as little troubleshooting and downtime as possible? This dissertation provides a complete and systematic methodology to efficiently detect and isolate faults within large, complex systems through the advancement of a technique called structural analysis. Structural analysis investigates the connections, or structure, between unknowns, knowns, and faults through the constraints (equations) of a system. The method hinges on an analytical model being converted into a structural model, represented by bipartite graphs or incidence matrices, which allow for detection and isolation properties to be investigated by decomposition. Open issues with structural analysis and model-based diagnosis including how to address diagnosis in systems that are under-constrained, how to optimize sensor placement in under-constrained and/or large-scale systems, and how to systematically address diagnosis in large-scale complex systems are addressed. The application that motivates this research is that of compressed air systems in industrial processes or plants. Compressed air systems are fully adjustable, do not interfere with electrical monitoring equipment, are able to operate in extreme temperatures, and can be stored in pressurized tanks or vessels (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Cheena Srinivasan (Committee Member); Tunc Aldemir (Committee Member); Ran Dai (Committee Member); Qadeer Ahmed (Committee Member) Subjects: Electrical Engineering; Mechanical Engineering