SHAREOK Repository
Not a member yet
49261 research outputs found
Sort by
2023-24 budget.
Get PDFThe University of Central Oklahoma submits its budget for the upcoming fiscal year to the Oklahoma State Regents for Higher Education for final approval. This document is a copy of the approved budget for FY24
Theory-enhanced automation of the digital publics' relationship assessments
Get PDFThe current dissertation aims to develop a Machine Learning (ML) method for automating the assessment of digital public relations by incorporating the Organization-Public Relationship Assessment (OPRA) developed from the public relations theory. The study targets customers/consumers and employees. For methods, Natural Language Processing (NLP) techniques, specifically text-embedding and classification, are used to analyze the crawled data and three survey data. The results demonstrate that TF-IDF, BERT embedding, and the SVM classification model perform best. The case study outcomes using TripAdvisor and Glassdoor review data validate the previous results. This dissertation project can serve as a pioneering effort to enhance the theoretical foundation of most current data analytics tools in public relations
Game theoretic algorithms for decentralized decision-making environments
Get PDFTraditional viewpoints on the decision-making environment have been centered on a top-down perspective, guided by a hierarchy in which only one entity may establish desirable criteria and pursue them while considering others to be quiet agents in the system, merely obeying instructions and judgments taken. Many research studies and mathematical formulations have been developed with this centralized approach in mind. Although systems in the past were not entirely centralized in architecture, researchers assumed this simplification because systems were less complex and with fewer interdependent relationships. Therefore, considering a centralized mathematical formulation describing such systems' behavior was sufficient. However, today's complex systems, which have a very high degree of interconnectedness, exhibit distinct dynamics as subsystems are increasingly dependent on one another. Additionally, considering the free market environment, which encourages reserving government function to the privet sector, the number of decision-makers with distinguished interests and profiteering behavior that can affect the system has been increased. Therefore, two types of interdependencies can be realized in dealing with today's complex systems: (i) physical interdependency among the systems, and (ii) interdependency among decisions made by the decision-makers in the system.
Focusing on these two interdependency types, this research proposes algorithms incorporating mathematical formulations addressing decentralized decision-making environments considering interdependent complex systems. The game theory concept has been utilized to address the decentralized decision-making environment, while permanent or temporal networks represent the interdependencies between the subsystems. This research has four research threads, each focusing on a decentralized decision-making environment where one can realize a kind of dependency among decision-makers and physical sub-systems. The first research thread, like the second, considers multiple decision-makers only interested in recovering their associated network post-disruption. However, the research addresses the problem of interdependent infrastructure network restoration where both decision-makers depend on each other. Therefore, the research develops an iterative algorithm on a game-theoretic basis, enabling to effectively search the solution space for possible Nash equilibria in the system. Also, we have illustrated that a lower bound could be discovered for each decision-maker’s best response in every iteration of the algorithm, where using that can reduce the number of algorithm iterations and, subsequently, calculation time.
The second research thread proposes an adaptive algorithm using machine learning to integrate predictions about other decision-maker behaviors into interdependent network restoration planning under an imperfect information-sharing environment. The proposed algorithm considers a permanent network representing a dependent infrastructure network where each network has a separate decision-maker (i.e., utility) only interested in and benefiting from the recovery of its associated network post-disruption. As one decision-maker is dependent on the other and lacks complete information about the leader’s restoration activities, a predictive model based on a machine learning approach was designed to predict the behavior of the leader decision-maker. The proposed algorithm provides a solution sufficiently close to the optimal solution showing the algorithm performs well in situations where the information sharing environment is incomplete.
The third research thread focuses on the impact of demand surge on critical supply chain networks due to disinformation propagation in the communities. In this research, we presented a framework that enables the modeling of these fluctuations and the evaluation of their impact on the adaptability of the supply chain. The objective was to identify any vulnerabilities or shortcomings within the supply chain by analyzing its flexibility in response to these variations.
The forth research thread focuses on a semi-decentralized, three-stage algorithm based on game theory to resolve potential conflicts among multiple aircraft travelling in a 3D shared airspace. Considering the aircraft pilots as decision-makers, the main objective of this research was to allow pilots to pursue their interests and have the liberty to select their flight characteristics freely while maintaining a safe distance from others. Conflicts among aircraft were considered logical, and temporal interdependency relationships existed between them, considering their route and flight characteristics
An Investigation Between Tornadic and Non-Tornadic QLCS Mesovortices using Operational and Experimental MRMS Products
Get PDFQuasi-linear convective system (QLCS) tornadoes have become an active area of research over the last several years. Through numerical simulations, it is theorized that QLCS vortex formation occurs near the surface in a quick response to heterogeneities along the baroclinic zone at the leading edge of the system. The mechanisms responsible for the development of vorticity in the lowest tens of meters involve storm-scale processes such as downdrafts, rear inflow jets, and friction from land interactions. The Multi-Radar Multi-Sensor (MRMS) systems contain operational algorithms that can observe storm-scale dynamic features taking advantage of the quality of the WSR-88D network. Blending numerous WSR-88D radars surrounding a target QLCS can provide a more complete three-dimensional image compared to a single radar view that captures storm-scale characteristics at both the low and upper levels, blended onto a 0.01 degree latitude by 0.01 degree longitude (~1 km) and 0.005 degree latitude by 0.005 degree longitude (~500 m) horizontal grid space. This study highlights testing of currently operational (e.g., dual-pol products and azimuthal shear) and experimental MRMS products (e.g., divergent shear and total shear) that aid in detecting QLCS meso-gamma-scale (2–40 km) vortices and subsequent tornadoes in both the pre-tornadic and tornadic phases.
A total of 107 tornadic and 139 non-tornadic mesovortices are examined over 13 QLCS events spanning from 2019 through 2022. It was found that tornadic mesovortices often display deep, transient plumes of significantly enhanced cyclonic shear relative to non-tornadic mesovortices during the pre-tornadic phase. Further, signals displaying a significantly higher magnitude of mid-to-upper level divergence and low-to-mid level specific differential phase (KDP) is illustrated, indicative of transient precipitation-loaded updraft pulses manifesting during the hour prior tornadogenesis. Additionally, the ambient environment in which tornadic mesovortices manifest are characterized with steeper most unstable lifted condensation level (MULFC) lapse rates and higher surface-based CAPE relative to their non-tornadic counterpart. Higher 0–3 km AGL and 0–6 km AGL are also evident in tandem with higher 0–1 km AGL and 0–3 km AGL storm relative helicity (SRH). Non-tornadic mesovortices are often characterized with a higher magnitude of cyclonic shear and convergent shear at the near-surface relative to their tornadic counterpart during the period prior to their peak in 0–1 km AGL layer-maximum azimuthal shear. High variability and significant overlap in the interquartile range exists for all operational and experimental products for all tornadic and non-tornadic mesovortices retained in the dataset, indicating that the processes involved in QLCS tornado formation can vary on a case-by-case basis.
A total of 107 tornadic and 139 non-tornadic mesovortices are examined over 13 QLCS events spanning from 2019 through 2022. It was found that tornadic mesovortices often display deep, transient plumes of significantly enhanced cyclonic shear relative to non-tornadic mesovortices during the pre-tornadic phase. Further, signals displaying a significantly higher magnitude of mid-to-upper level divergence and low-to-mid level specific differential phase (KDP) is illustrated, indicative of transient precipitation-loaded updraft pulses manifesting during the hour prior tornadogenesis. Additionally, the ambient environment in which tornadic mesovortices manifest are characterized with steeper most unstable lifted condensation level (MULFC) lapse rates and higher surface-based CAPE relative to their non-tornadic counterpart. Higher 0–3 km AGL and 0–6 km AGL are also evident in tandem with higher 0–1 km AGL and 0–3 km AGL storm relative helicity (SRH). Non-tornadic mesovortices are often characterized with a higher magnitude of cyclonic shear and convergent shear at the near-surface relative to their tornadic counterpart during the period prior to their peak in 0–1 km AGL layer-maximum azimuthal shear. High variability and significant overlap in the interquartile range exists for all operational and experimental products for all tornadic and non-tornadic mesovortices retained in the dataset, indicating that the processes involved in QLCS tornado formation can vary on a case-by-case basis
Educated Decision-Makers are Harder to Bias: Comparing Education and Nudges on Reliable Deliberation about Recycled Water
Get PDFRecently, it has become important to define ethical rules governing the most appropriate ways to interact with those that decision interventions seek to influence. However, philosophical theories and current methods of comparing interventions in the decision sciences are likely inadequate to fully demonstrate ethical differences among various types of interventions. Here, I seek to address this problem by first proposing a framework (based on the American Psychological Association’s ethical code of conduct) for criteria that should likely be considered when making ethical comparisons among interventions. Then, I propose a method through which one could identify the extent to which nudges and educational interventions promote reliable deliberation—a condition that has been argued to be central to ensuring autonomous decision making—when making decisions regarding recycled water. In four experiments, I show that nudges and educational interventions can both be used to shift individual preferences regarding recycled water, but that only educational interventions result in greater choice consistency, a factor that I propose is integral to demonstrating one has deliberated reliably. These results are likely of important practical benefit, as they might guide policymakers and water practitioners towards interventions that are likely to result in consistent and stable public support of recycled water, which could help avoid costly consequences such as protests and legal challenges. They likely also have important ethical implications, as they demonstrate a condition on which some nudges might fail to respect autonomy to the same extent as educational interventions. These results help us move one step closer to being able to empirically quantify ethical risks and benefits of using different intervention strategies and represent an important stepping-stone in defining an integrated ethical interaction theory
Kusama's Polka Dots and Kawara's Dates: Finding American Success Through Differing Uses of Identity, Temporality, and Artistic Expression
Get PDFAmerican art spaces have been established as areas that allow for greater acceptance and recognition for artists thus drawing in artists from multitudes of cultural backgrounds. Due to social unrest and change during the mid-20th century, an influx of non-Western artists brought Japanese artists seeking artistic recognition and greater acceptance into American spaces. To effectively navigate cultural exchange in order to gain greater recognition, Japanese artists must learn to create, establish, and bring their art into Western spaces in manners that encourage American consumption. A process that is difficult and few succeed in, artists such as Yayoi Kusama and On Kawara, two widely successful Japanese artists in America, have learned how to bring about such success by incorporating their identity, temporality, and their own artistic interpretations into their art creation. Such accomplishments have allowed for greater Western recognition, changed how Japanese art is viewed in Western spaces, and solidified their places as revered artists in the United States. Although Western recognition is difficult to accomplish and does not follow a specific set of rules, it is apparent that both artists have navigated the American art world successfully
The multimodal rhetoric of Tinder and the algorithms that shape our choices
Get PDFAs the popularity of online dating applications continues to grow in the digital world, so does the use of algorithms and multimodal rhetoric in shaping user experiences. Both multimodality and algorithms are related to online dating. Dating app profiles are multimodal in the way they incorporate different modes of communication such as images, texts, and videos. Users are presented with a swipe stack and can indicate their level of interest in other users by swiping left to dismiss the profile or right to express interest. Algorithms rely on the multimodality of these dating profiles to match the user's preferences and interests which can create a more personalized experience. The algorithm is controlling who might show up in the swipe stack, which means users are in control of the information they put on their profiles, but not in control of who they are seeing. The lack of transparency from dating apps like Tinder regarding the inner workings of their algorithm raises questions about the selection of profiles that users are presented with in their swipe stack. Despite many rumors surrounding the workings of algorithms, such as the one used by Tinder, little is known about their exact functionality.This thesis conducts a comprehensive analysis of Tinder profiles to gain a better understanding on how the app's algorithm influences our online behavior. The most significant finding demonstrates that one of the algorithm's major functions is to mirror users' profiles back to them. That is, regardless of the preferences and multimodal content a user inputs into the app, the algorithm seems to show the user profiles that mirror their own. This thesis contributes to the critical understanding of understanding multimodal composition and the role algorithms play in shaping online dating experiences and the love lives of Tinder users
Exploring the toxic triangle: the effects of leadership, team mental models, and core self evaluations on follower sensemaking and ethical decision making
Get PDFLeadership is an omnipresent aspect of daily life, particularly in organizational settings. While research has extensively examined constructive leadership and its effects on follower and organizational performance, there is a burgeoning interest in exploring the domain of destructive leadership. This has led to the development of the toxic triangle model, which focuses on the interaction between destructive leadership, susceptible followers, and conducive environments. However, more research is necessary to comprehend how these elements interrelate and influence significant outcomes. Thus, the current study aims to investigate the toxic triangle in a simulated organizational context by examining the impact of destructive leaders on the sensemaking and ethical decision making processes of followers, while considering the roles of follower core self-evaluation and perceived team mental models. Participants complete a range of measures and confront an ethical dilemma in a low-fidelity marketing scenario. By exploring the effects of destructive leadership in the presence of follower and contextual vulnerabilities outlined in the toxic triangle theory, this study seeks to advance understanding in this nascent area of research and discuss the implications of findings for leadership, organizational contexts, and literature.
Keywords: leadership, destructive leadership, core self-evaluation, team mental model, sensemaking, ED
Graph Attention and Persistence for Traveling Salesman Problem
Get PDFCombinatorial optimization problems have long been a computationally-challenging family of problems with high importance within science. Although algorithms to solve such problems exist, these classical/exact algorithms tend to become computationally intractable as the problem size increases. Due to the relevancy of such problems in real life, research has explored other algorithms that forego the optimality guarantee of classical algorithms. In doing so, heuristic algorithms have achieved fast inference times with performance that is not too far from the optimal performance of classical algorithms. A particular heuristic algorithm from artificial intelligence, the attention model (AM), has achieved state-of-the-art performance in prevalent combinatorial optimization problems such as the traveling salesman, vehicle routing, and orienteering problems, where the gap between classical and heuristic algorithms was diminished. This success is attributed to the ability of the AM to tend to the structure of the input through the use of graph attention (GAT) mechanisms, a type of graph neural network. While the mechanism by which these models extract structural information, it is unknown what kind of information is extracted. This thesis presents a novel variant of the attention model (AM), the persistence attention model (AM-P), which explicitly uses a type of structural information, persistent homology. The model is tested on the traveling salesman problem with different problem node counts to compare the performance of the attention model with and without persistent homology information. It is hypothesized that persistent homology information will help the attention model better exploit the structure of the model, achieving better performance on combinatorial problems. This hypothesis is demonstrated false as there is no statistically-significant differences between the performance of the AM and AM-P. This negative result raises the possibility that the attention model may already extract structural information similar to persistent homology through its GAT mechanisms
Parental stress and self-care
No full textStress is a concerning contributor to non-optimal parenting practices. Why is educating parents on self-care not an evidence-based practice? Research indicates stress impacts parenting in negative ways. More specifically, stress has been linked to the use of more harsh parenting practices (Chung et al., 2020). These changes in parenting behaviors have the potential to effect children's development. Children may have an increased chance of long-term illnesses (Center for Disease Control and Prevention, 2019), hindered development of higher cognitive processes (Harvard Health, 2020), and behavioral problems (Neece et al., 2016). There is little known about the effects of self-care on parental stress. Literature on self-care suggests it is beneficial in preventing burnout in mental health providers (La Mott & Martin, 2016) and coping with mental illnesses such as schizophrenia (Martyn, 2003) and depression (Khan et al., 2007). Related to parent education, the Triple-P Positive Parenting Program mentions self-care briefly suggesting that being a parent is personal self-care (Sanders, 1999). However, the current study aims to explore self-care as activities parents do to alleviate general stress. The purpose of this study was to determine if there is a correlational relationship between parental stress and self-care practices. It was hypothesized that parents reporting higher levels of stress would have lower self-care scores. A correlational study design was most appropriate for this research because it allows for several variables to be measured. Two measurement tools, Perceived Stress Scale (Cohen et al., 1983) and Mindful Self-Care Scale (Cook-Cottone & Guyker, 2017), were administered to participants utilizing the Qualtrics software. The findings of this study found a significant negative correlational relationship between parental stress and self-care, confirming the hypothesis. Future research can expand upon this in investigating whether educating parents on self-care lowers their reported stress scores