SHAREOK Repository
Not a member yet
49261 research outputs found
Sort by
Artificial Intelligence Aversion in Public Policy
Get PDFThis dissertation advances our understanding of how individuals relate to the utilization of artificial intelligence within the public policy domain. This is done through theoretical development about the aversion individuals have towards artificial intelligence as well as through empirical examination of that artificial intelligence aversion and how it manifests. In this dissertation I identify that artificial intelligence exists as a unique concept to individuals in contrast to algorithms. I then develop an index to measure people’s levels of aversion to artificial intelligence. I validate that index by assessing how well it does at predicting support for current and future uses of artificial intelligence.
Once my index is validated I then turn toward trying to understand what variables are contributing towards people’s different levels of aversion. I first examine the role that perceptions about risk and subjectivity of the area the artificial intelligence is being used in has on people’s aversion index scores. I then examine how demographics influence both perceptions as well as people’s levels of aversion. In these examinations I find that: perceived risk and perceived subjectivity contribute in part to people’s levels of aversion, with perceived risk having a larger effect; and that demographics play a key role in people’s perceptions about the utilization of artificial intelligence. Demographics help to understand how personal levels of aversion to artificial intelligence differ and are identified as an important area of focus if policy makers want to reduce artificial intelligence aversion. This research paves the way for future examination into how aversion changes over time as artificial intelligence is increasingly utilized in people’s lives
Wet Bulb Globe Temperature and Associated Heat Waves in the United States Great Plains
No full textHeatwaves are a leading contributor of weather-related mortality, globally contributing to thousands of deaths each year. The impacts on humans may be direct or indirect through avenues such as heat stress, strained medical capacity, infrastructure breakdown, and reduced crop yields. While extreme heat is often measured by temperature and humidity, Wet Bulb Globe Temperature (WBGT) is commonly used to evaluate real-time heat stress risks in humans and correlates better with heat related illness, and is used by the Occupational Safety and Health Administration (OSHA) and the US Army. WBGT is a weighted average of air temperature, natural wet bulb temperature, and black globe temperature. A local hourly, daily, and monthly WBGT climatology will allow those planning outdoor work to minimize the likelihood of heat related disruptions. Further, understanding the characteristics of heat waves will allow emergency planners and responders to know what to expect when heat waves occur. Additionally, evaluating the predictability of WBGT heat waves allows an understanding of what advance warning may be possible and when confidence may be higher.
In this study, WBGT is calculated from the ERA5 reanalysis and is validated by the Oklahoma Mesonet and found to be adequate. Two common methods of calculating WBGT from meteorological observations are compared. The Liljegren method has a larger diurnal cycle than the Dimiceli method, with peak WBGT about 1 °F higher. The high and extreme risk categories in the southern United States Great Plains (USGP) have increased from 5 days per year to 15 days from 1960-2020. Additionally, the largest increases in WBGT are occurring during DJF, potentially lengthening the warm season in the future. Heat wave definitions based on maximum, minimum, and mean WBGT are used to calculate heat wave characteristics and trends with the largest number of heat waves occurring in the southern USGP. Further, the number of heat waves is generally increasing across the domain. This study shows that heat wave days based on minimum WBGT have increased significantly which could have important impacts on human heat stress recovery.
The predictive skill of WBGT heat waves is evaluated using ERA5 reanalysis and models from the S2S Project Database. North American atmospheric regimes are defined using K-Means clustering of detrended standardized 500 mb geopotential anomalies from ERA5 reanalysis. Additionally, heat wave types (e.g. Hot-dry or warm-humid) are defined using the standardized anomalies of temperature and humidity relative to other heat waves during the same season. An analysis of the predictive skill
of atmospheric regimes, heat wave types, seasonality, and the impact of ENSO and the MJO is conducted using these datasets both with zero days lead time and at S2S lead times. Finally, regime statistics are calculated in S2S model forecasts to identify skill and forecast biases. Each regime has unique heat wave frequency, type, and seasonality characteristics. Heat waves may occur in the US Great Plains with greater than twice the climatological frequency in some regimes, however the increase is often seasonally dependent. Additionally, some skill is shown at discriminating between heat wave types in different regimes. Further, the regimes that are conducive to heat waves at short lead times differ from those that correlate with heat wave occurrence at longer lead times. When incorporating the ENSO or MJO phase heat wave in addition to the atmospheric regime, some additional predictive skill is observed, with the MJO providing more skill, as it introduces larger variation between regimes and provides information regarding timing of individual heat waves where the ENSO phase does not due to the much longer period. Combining both the ENSO and MJO phase provides the highest skill, with some ENSO/MJO/regime combinations having historically observed heat wave rates in excess of 50% at some lead times while other combinations are near 0% historically, thus leading to forecasts of opportunity when a signal for higher heat wave rates occurs. S2S models show statistically significant skill in forecasting most regimes up to 5 weeks lead time. However, the accuracy at predicting the correct atmospheric regime may not be useful beyond 1-2 weeks, which may limit the ability to incorporate regimes into heat wave forecasts at longer lead times, as this information is necessary for combination statistics that incorporate regimes
Multimodal Imaging Approaches Using Functional Near-infrared Spectroscopy, Electroencephalography and Transcranial Magnetic Stimulation
No full textSimultaneous functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) is a neuroimaging device used to record hemodynamic and electrical responses to stimulus. While this imaging system is used amongst many populations, it is used for the first time in epithelial ovarian cancer patients to assess chemotherapy induced cognitive impairment. Before imaging, cognitive side effects were only qualitative in that subjects would report memory loss, brain fog, and general slowness; however, using fNIRS-EEG has quantified the chemotherapy-related changes in neurological function.
Upon successful clinical outcomes from fNIRS-EEG, further treatment applications were perused. Thus, fNIRS-EEG was integrated with transcranial magnetic stimulation (TMS) for the first time into a simultaneous neuroimaging and modulating device (fNET). From one successful pilot study, fNET is pioneering the way of many other disease therapies: including depression and glioma treatment. Specifically, fNET is developed to synchronize TMS to alpha phase in real time, which is to be used in new depression therapies. fNET is also developed to aid in brain tumor surgical planning and set healthy baseline connectivity data using working memory paradigms.
In this thesis, each of the three experiments are discussed in chronological order to illustrate how multimodal fNET has been developed. By integrating TMS with previously established fNIRS-EEG, neuroimaging possibilities are being engineered into the next standard of clinical care
A Seasonal to Subseasonal Examination of Synoptic and Local Drivers of Flash Drought
Get PDFFlash drought is the rapid intensification of drought like conditions. Initiated by meteorological processes that quickly desiccate soil they can be devastating to agriculture and local ecosystems. Flash droughts are driven by a complex interaction of many terrestrial and atmospheric processes making prediction challenging. The prediction of future flash drought events will require a better understanding of these complex interactions. In particular, synoptic scale processes that lead to changes in evaporative stress are poorly understood, and little is known as to how they impact the local processes. To better predict flash drought both remote teleconnections and the effects of evaporative stress on local terrestrial conditions must be identified. Additionally, the temporal lag from remote drivers to flash drought development needs to be quantified.
Using current datasets, multiple methods focusing on synoptic and local spatial scales from seasonal to sub seasonal temporal scales were used to increase our understanding of conditions that drive flash drought. Seasonal atmospheric variables were used to identify areas in Europe primed for agricultural flash drought development in a later season. Additionally, sea surface teleconnections and synoptic scale atmospheric processes were examined before and during flash drought development to identify remote drivers. Lastly, the identified remote drivers were mathematically and statistically assessed for both their covariance with evaporative stress and their correlation with changes in evaporative stress in the Central US
Exploring Entrepreneurial Mindset and Characteristics: Their Influence on Readiness, Success, and Educational Implications
Get PDFEntrepreneurship has historically taken the inadequate and misleading misnomer of starting a business. The concept is far more significant and encompasses much more than the act and its process. Typically, the critical missing piece of the subject matter is the individual behind the enterprise–the entrepreneur. Insufficient attention is given to the business proprietor or entrepreneur (one who undertakes an endeavor) and, by extension, their mindset, readiness, skills, and tools to innovate our lives, economy, and world. The field of entrepreneurship and its proprietor merit further specific study. Expressly, the entrepreneur's mindset and readiness warrant attention, study, and analysis. This research study explores the characteristics that predict entrepreneurial success, including mindset and readiness.
Independent variables such as communicator/networker, education, motivation, risk taker and control are analyzed in this study to view their effects on entrepreneur and business success. The independent variables are studied and considered in the field of education. The communicator/networker, education, motivation, risk taker and control are independent factors in this context. The study concludes by grouping the essential constructs from six significant independent variables into a survey tool used by educators, administrators, admissions personnel, and other interested parties to attract and filter potential students for entrepreneurial study programs.
By studying critical determinants such as mindset, readiness, and skills, this research underscores the need for a holistic approach to entrepreneurship education. It advocates for curricula that extend beyond traditional business topics to encompass a broader array of competencies. Furthermore, developing a survey tool synthesized from this research offers a practical mechanism for identifying and selecting prospective entrepreneurial students. This contribution underscores the significance of the entrepreneur in fostering innovation and societal change, providing valuable insights for future research and practice in entrepreneurship
Cloudy with a Chance of Conservation: How Perception Shapes Conservation Behavior
Get PDFWith disruptive natural events such as hurricanes, flooding, and drought-based wildfires affecting ecosystems and human communities more frequently than ever, environmental conservation is a critical issue now and going forward. In this study, I assert that individual perceptions of natural events, weather, and local climate are linked to differing attitudes and behavior related to conservation. Using the complementing interdisciplinary theories of social construction of space, construal-level theory, and social capital theory, I investigate the relationship between individual weather perception and environmental conservation behavior independent of common social, political, and ideological variables. Results from poisson regression models using data from the Oklahoma Center for Risk and Crisis Management’s 2018 national survey “Weather Society and Government” demonstrate a clear positive relationship between the perception of disruptive natural events and conservation actions. Furthermore, results show that social capital is an important moderator in this relationship. Specifically, the relationship between weather perception and conservation behavior is more pronounced for those with higher reported social capital. This study underscores the importance of environmental perceptions and social connections in predicting individual conservation behaviors
AN APPROACH TO SIMULTANEOUS WIRELESS SYNCHRONIZATION AND NAVIGATION FOR MOBILE DISTRIBUTED NETWORKS OF RADAR SYSTEMS
Get PDFRecent developments in all-digital phased arrays have probed the upper bound of performance for single monostatic radar system performance. In order for radar system performance to continue to improve beyond the current state-of-the-art, it is imperative that research into the implementation of distributed radar systems be performed as such systems will enable significant performance enhancements in comparison to traditional monostatic radars. For these systems to be implemented, particularly in mobile scenarios, both the accurate navigation and synchronization of the systems must be performed. These processes must be performed at the carrier wavelength accuracy which poses a strict requirement on the performance of the navigation and synchronization algorithms. These components have received significant attention in the literature. However, although they are closely related problems for implementing mobile distributed radar networks, the potential for implementing algorithms for simultaneous navigation and synchronization has largely been unexplored. Therefore, the research proposed in this dissertation aims to implement algorithms for simultaneous navigation and synchronization for distributed radar networks by leveraging time-of-flight (TOF) ranging signals and associated Doppler measurements.
This dissertation provides a comprehensive literature review of current techniques for achieving navigation and synchronization solutions. A background is provided, describing linear and nonlinear Kalman filtering for time-series state estimation, relevant propagation effects on radio signals, a mathematical framework for inertial navigation, and the radio frequency (RF) synchronization signal model. Current research results in cooperative navigation for radar motion compensation are presented. A novel algorithm for time, phase, and frequency synchronization is described which is capable of achieving synchronization exclusively in software, enabling decentralized implementation in existing radar systems. This algorithm is provided for a single iteration of synchronization and is also adapted for the Kalman filtering of the synchronization states. A framework for combining the cooperative navigation and synchronization routines is provided along with simulated results and analysis on performance. A preliminary hardware demonstration of the synchronization algorithm is also given. Finally, a summary of future research direction and goals is provided along with a conclusion to the dissertation
Education Reform for the Everyman Philosopher: Themes in Montessori Pedagogy and their Ideological Resonance
Get PDFThe progressive education movement broadly elevates reforms centered around individualized instruction and social consciousness. Montessori schooling presents a uniquely successful case where organized nonprofits have facilitated notable expansion in recent years, thus begetting two intertwined questions: could contentious education politics harm the Montessori movement going forward? Moreover, what specific values guide leading proponents’ advocacy? I engage in exploratory research to address these topics. First, I draw upon national survey results to uncover ideological and demographic determinants of Montessori support. This work unearths a consistent inverse association between conservative political ideology and favorability toward key aspects of the Montessori method. Secondly, I leverage conceptual categories derived from Moral Foundations Theory in the quantitative content analysis of prominent Montessori nonprofits’ website-based public communications. Relative moral term usage across organizations exhibited some high-level similarities but was often significantly different in formal comparisons. Together, both analytical strategies highlight and contextualize the emergent need to determine whether specific teaching methods evoke meaningful ideological reactions from stakeholders
A Longitudinal Comparative Analysis of Inequality Trajectories in OECD Countries during the Great Recession and Covid-19 Pandemic Eras, 2002-2021
No full textThis dissertation investigates income, labor, health, and gender-based labor inequalities across Organization for Economic Cooperation and Development (OECD) countries, emphasizing how these disparities are exacerbated by the COVID-19 pandemic and the Great Recession. Employing a longitudinal comparative methodology grounded in Exogenous Shocks theory, Institutional theory, Welfare Regime Theory, and Skill-Biased Technological Change, this study employs Exploratory Data Analysis of time-series, Mixed Models for Repeated Measures (MMRM), and Generalized Estimating Equations (GEE) to identify evolving trends during these crises, assess their impact on welfare regimes, and conduct detailed analyses of gender-related labor market disparities.
The findings reveal rising inequalities, with notable spikes in income disparity in specific countries, heterogeneous trends during the crises, and varied effects on labor force inequality across countries. The pandemic exerted a substantial, though divergent, influence on health disparities, highlighting the widespread impact on income, labor, health, and gender labor disparities across OECD countries.
This research also evaluates the impact of global events on welfare regimes, uncovering diverse effects on income inequality and fluctuations in employment and unemployment rates. Findings indicate a decline in life expectancy across all regimes during the pandemic, accompanied by increases in infant mortality rates compared to the Great Recession period. The study highlights the varied influence of global crises on welfare regimes and their associated socio-economic disparities. Additionally, it undertakes a comparative analysis of gender labor inequalities during these periods, identifying a significant influence of the pandemic on the gender employment gap, differentiated effects on gender employment disparities, and a unique interplay between the pandemic and the gender unemployment gap. Gross Domestic Product (GDP) emerged as a critical determinant, elucidating the overarching economic dynamics that influence gender disparities in the labor market during these crucial times. Finally, this study elucidates empirical evidence suggesting pathways for mitigating observed inequalities across OECD countries. It emphasizes the importance of reinforcing labor markets, bolstering gender equality in occupational contexts, and methodically revising economic paradigms to align with current and future needs. Moreover, the research posits that evaluating existing welfare regimes could yield a framework adept at confronting the multifaceted challenges posed by globalization, thereby enhancing societies' adaptive capacity to global crises
Principals' Perceptions of Isolation and Contributing Factors: A Mixed Methods Analysis
Get PDFPrincipals’ perceived isolation has been empirically associated with factors such as lower self-efficacy, job dissatisfaction, and burnout and a strong predictor of a principal's intentions to leave the profession. The purpose of this study was to examine the prevalence of feelings of isolation among school principals, and to explore what organizational structures and control mechanisms may influence those feelings through a mixed-methods approach. The data collection and analysis included a principal survey of both their perceived isolation and social disconnectedness followed by 12 semi-structured interviews to explore principals’ experiences with isolation from within their organizational constructs and professional roles. This study found a relatively low prevalence of isolation, both socially and emotionally in the professional context of the principalship. However, of those principals who felt isolated, it was often purposeful; they report choosing isolation in their personal lives as a means of self-preservation or protection. This selective isolation seemed to be a result of paranoia and professional risk that the principals perceived to exist in their personal lives and relationships as a result of their professional identities.
Keywords: principalship, isolation, social integration, social provisions, loneliness, principal support, principal well-bein