Assessing the Role of Employees\u27 ESG Perception on Turnover Intention: A Moderation Analysis

This dissertation examines the relationship between employees’ perceptions of their organization’s ESG performance and turnover intentions by drawing upon Social Identity Theory (SIT). SIT suggests that individuals internalize organizational values and beliefs through organizational socialization, which strengthens their identification with the organizations, leading to support for organizations that embody those values. This study also investigated whether ESG awareness is a better predictor than ESG perception. The research employed a cross-sectional survey design to examine whether age and political affiliation moderate the relationship between ESG perception and turnover intention. The findings suggest that employees’ perceptions of their organization’s ESG performance are negatively associated with turnover intention, and political affiliation plays a significant role in moderating the relationship such that the interaction was stronger for individuals who identified more strongly with the Republican Party. The comparison of models using ESG awareness versus ESG perception scales revealed that the model with ESG awareness slightly outperformed the model with ESG perception despite lacking significance

Quarterback Statistics vs. Season Success

The purpose of this research is to determine which quarterback statistic most significantly impacts team success in the National Football League. By analyzing data from quarterbacks with at least 100 pass attempts per season from 2006 to 2023, we examine the relationship between quarterback rating, passer rating, completion percentage, and TD-INT ratio with end-of-season power rankings. We ran the data through multiple linear regression models to identify which statistic has the strongest correlation with team performance. Our model considers variations across different seasons and accounts for statistical trends over time. With over 17 seasons of data analyzed, further exploration could refine the findings by incorporating additional variables or alternative modeling approaches

A Preliminary Report of the Carotenoid Assessment in Adults 50 Years of Age and Older in Louisiana

Fruits and vegetables are at the core of a healthy, balanced diet. They assist in preventing the development of chronic diseases such as hypertension, diabetes, cardiovascular disease, and cancer (Fadnes, et al., 2022). Aging is a natural biological process followed by disease, cognitive impairment, and a natural decline of the body’s ability to perform at its best. The older adult population is currently the fastest-growing population globally (El-Shebiney et al., 2022). Vitamin A is crucial for several parts of the body to function correctly. Despite the positive effects of Vitamin A on the body, the recommended dietary intake is not being met in the United States. This is especially important in the growing population of adults ≥ 50. Educating this population on the importance of a healthy lifestyle and diet is crucial in preventing the onset of chronic diseases and other age-related issues. This study assessed the consumption of Vitamin A-rich fruits and vegetables in adults ≥ 50 residing in Northwest Louisiana using the Veggie Meter® device and selfreported fruit and vegetable intake. This cross-sectional research design included a researcher-developed electronic questionnaire and non-invasive physical measurements of skin carotenoids. Participants were recruited via email invitations, flyers, and personal contacts. Eligible participants include those ≥ 50 years of age who are free-living, noninstitutionalized, and can self-report food intake. Data was collected using the Veggie Meter® Instrument and through Qualtrics Survey Software. The survey consisted of 5 demographic items, 11 health and vitamin supplement items, the REAP-S diet quality tool (14 items), six eating environment items, three medication items, three diet counseling items, and a Vitamin A food frequency component modeled after a previous study conducted in the Nutrition and Dietetics program at Louisiana Tech University (Putnam et al., 2023). A total of fifty-nine adults participated in this study. However, only fifty-six completed the electronic questionnaire and all non-invasive Veggie Meter® measurements. The majority of participants were females (66.1%), and 33.9 percent of participants were males. The majority of participants were White and Non-Hispanic (98.0%), while the smallest proportion of race was African-American or Black (0.03%). The mean age of participants was 67, while participants ranged from ages 52 to 84 years of age. Several findings were revealed in this study. It was reported that males had higher mean carotenoid scores than females; there was a significant positive correlation between food frequency scores, total REAP-S scores, and mean carotenoid scores. There was no correlation between BMI in older adults ≥ 50, which is a different finding from that of younger adult populations. There was no difference in carotenoid scores between age quartiles; however, the sample size was small. There was a positive correlation between REAP-S diet quality scores and participants who rarely skipped breakfast. Participants who rarely skipped breakfast scored higher on the REAP-S portion of the questionnaire. Those who saw a Registered Dietitian for any reason in the past scored significantly higher on the REAP-S portion of the questionnaire, which resulted in a positive correlation. There was a negative correlation between age and eating out. As age increased, the number of times participants ate meals outside of the home per week decreased. Unlike in a previous study, no significant correlations were found between this sample and BMI. The REAP-S items regarding fruit and vegetable intake revealed that 68% of participants are not eating ≥ two servings of fruit per day and 79% are not eating two or more servings of vegetables per day Expanding the size and diversity of participants and conducting more research could give insight into more appropriate interventions for this population. By implementing more educational pieces to the puzzle, adults 50 years of age and older could develop healthier habits overall and increase their quality and longevity of life

Personality, Self-Compassion, and Alcohol Use in College Students

Substance use has been occurring throughout history, and most individuals have been impacted by substance use in one way or another (Durrant & Thakker, 2003). The individual using substances is not the only one who faces the consequences of their usage. Their families and communities also pay a price (Lander et al., 2013; McLellan, 2017). College students are in a unique role in their lives where they are fighting for autonomy, trying to fit in with their peers, and facing new stressors they have never experienced before. Previous research has demonstrated that certain personality traits, including conscientiousness and agreeableness, are associated with the development of substance use (Dash et al., 2019). Self-compassion can act as a protective factor for the development of a substance use disorder (Phelps et al., 2018). This study aims to fill some knowledge gaps and explore whether self-compassion acts as a moderator between certain personality traits, specifically conscientiousness and agreeableness, and alcohol use among college students. This study consisted of 488 college students collected from a medium-sized southern university. The results of this study indicated that self-compassion does not moderate the relationship between conscientiousness or agreeableness and alcohol use in college students. Implications of the study’s findings, limitations, and future directions are discussed in Chapter 4

Analyzing the Relationship between Diabetes Duration and Self-Management in Adults with Diabetes

Diabetes mellitus (DM) is a metabolic disease that affects 14.7% of adults in the United States (Centers for Disease Control and Prevention, 2023). Prevalence of diabetes increases with age, with rates for adults aged 65 and older reaching 29.2% (Centers for Disease Control and Prevention, 2023). Factors such as age, race, obesity, physical activity level, and genetics strongly increase the risk of developing type 2 diabetes (T2DM) (National Institute of Diabetes and Digestive and Kidney Disease, 2016). Diabetes requires complex and comprehensive management to reduce the risk of developing comorbidities (Al-Shabeeb et al., 2021). Factors such as education, familial support, socioeconomic status, attitude, and knowledge regarding the disease impact management ability (Duke et al., 2008; Borba et al., 2019). An inability to master diabetes management can lead to distress and burnout, causing an individual to neglect diabetes self-care behaviors (Jafari et al., 2024). As individuals age and become more familiar with their disease, standardized education techniques may not be as effective, especially if individuals are facing burnout. Interventions from Registered Dietitian Nutritionists (RDNs) and other diabetes healthcare professionals can alleviate the stress of diabetes management by improving diabetes health literacy (DHL) and self-efficacy and by providing needed support to individuals (Dobrow et al., 2017; Jafari et al., 2024; Marinic et al., 2017; Sbroma et al., 2017; Warner et al., 2018). While previous studies have examined the impact of other barriers on effective diabetes management, the influence of time since diagnosis on diabetes management has not been closely researched (Borba et al., 2019). This study aimed to assess the correlation between the duration of diabetes and self-management scores and HbA1c levels in adults diagnosed with diabetes. This study also aimed to determine the impact of RDN education interventions on self-management level and HbA1c levels. Respondents were recruited through social media and flyers that contained a link to an online survey. Flyers were advertised in primary care and diabetes specialist offices, community health centers, diabetes support group locations, free medical clinics, and diabetes outpatient facilities. Data collection lasted approximately four weeks. There were 238 respondents to the survey, and 216 responses were analyzed. Fifty-six percent of respondents were female, and 61.6% of respondents were white. Ninety-two percent of respondents were insured at the time of data collection, and 88.4% of respondents had health insurance coverage at the time of their diagnosis. Self-management level was determined using the Diabetes Self-Management Questionnaire (DSMQ-R). Participants’ responses were scored from 0 to 10 using the scoring guide. Respondents self-reported their most recent HbA1c level. Respondents were also asked if they had ever received education from an RDN, where they received the education, and how many times they received education. A Pearson’s correlation test was used to determine the correlations between the DSMQ-R score and continuous variables. The DSMQ-R total 20-item score was not correlated with years since diagnosis in this sample. Respondents with a higher total score on the DSMQ-R tool were more likely to be using intensive insulin therapy (r = -.198, p \u3c .05). Age and years since diabetes diagnosis were positively correlated (r = .306, p = \u3c .05). The average HbA1c level was 6.77% (SD = 1.10), with 64.8% of HbA1c responses being taken within the last three months. Sixty-five percent of respondents reported using intensive insulin therapy, while 33.8% reported using non-intensive insulin therapy. The average amount of RDN education sessions was 3.21 (SD = 2.68). The average DSMQ-R20 score was 4.60. Respondents were also mostly satisfied with their ability to afford and obtain prescribed diabetes medications (afford: M = 7.08, SD = 1.68; obtain: M = 7.22; SD = 1.71). When asked how they felt about managing their diabetes on a daily basis, 48.1% of respondents reported that they understand the need, but it can be challenging. A regression analysis was conducted to determine if variables could statistically significantly predict the DSMQ-R20 score. Age, cooperation with the diabetes team subscale, and eating behavior subscale were shown to be statistically significant (p \u3c .034, p \u3c .001, p \u3c .001, respectively). In contrast, years with diabetes, RDN education sessions, and the use of intensive insulin therapy were not statistically significant. Independent samples t-tests were conducted to determine if there were differences in eAG, HbA1c, DSMQ-R total scores, and DSMQ-R subscales between respondents who had been seen by an RDN and those whom an RDN had not seen. It was found that respondents whom an RDN had not seen had a slightly higher DSMQ-R27 score compared to respondents who had been seen by an RDN, which contradicts published research (M = 5.07, SD = 1.06); t(121) = -2.45, p = .016. An independent samples t-test was conducted to determine if there were differences in DSMQ-R scores and RDN education sessions between respondents with low A1c levels (≤ 6.49) and those with high A1c levels (6.5-13.0). There was a significant difference in DSMQ-R20 scores for respondents with high A1c (M = 4.54, SD = 1.19) and low A1c (M = 5.43, SD = 1.95) levels; t(58) = 2.9, p = .005. These results suggest that A1c level does impact self-management scores in individuals using non-intensive insulin therapy. There was a significant difference in the cooperation with diabetes team subscale scores for high A1c (M = 4.55, SD = 1.75) and low A1c (M = 5.56, SD = 2.29) levels; t(65) = 2.71, p = .009. Respondents with an A1c ≤ 6.49 also had statistically significant higher eating behavior subscale scores (M = 5.89, SD = 1.81); t(174) = 4.03, p = \u3c .001. The results of this study showed that the duration of diabetes is not correlated to self-management levels and HbA1c levels in this sample. Respondents who scored higher on the eating behavior subscale were less likely to be using intensive insulin therapy. In contrast, respondents who scored higher on the DSMQ-R tool were more likely to be using intensive insulin therapy. It was shown that respondents whom an RDN had not seen had a slightly higher DSMQ-R27 score compared to respondents whom an RDN had seen in this sample. These results suggest that respondents using intensive insulin therapy whom an RDN has seen have a slightly lower self-management score. Age and duration of diabetes were positively correlated with the eating behavior subscale. Respondents who have had diabetes for longer were shown to have a higher score on the eating behavior subscale. Respondents with a low HbA1c were found to have a statistically significant higher eating behavior score than respondents with a high HbA1c. These findings support the need for diet education to improve glycemic levels

Smoothed Particle Hydrodynamics for Free-Surface Flows and Time Series Forecasting Approach for Computational Fluid Dynamics

With the increase in computing power, numerical simulation has become an essential approach to solving problems in engineering and science. Numerical simulations provide a platform for theoretical validation and facilitate novel discovery. Even though extensive mesh-based numerical methods are utilized, significant limitations exist, particularly in Computational Fluid Dynamics (CFD). Because of the grid distortion, issues related to large deformations, moving interfaces, and free surfaces may lead to considerable computational errors, constraining their efficacy in numerous applications. As a mesh-free method, Smoothed Particle Hydrodynamics (SPH) was introduced in 1977 and has been widely applied in many fields such as astrophysics and hydrodynamics (D. a. Liu 2015). Free surface flow problems are covered in various domains, including hydraulic engineering, mechanical engineering, ship hydrodynamics, and petrochemical engineering. Hence, studying the free surface flow problem has theoretical and practical significance. Due to the advantages of SPH in handling large deformations and free boundaries, SPH is particularly suitable for free surface flow problems. Furthermore, new achievements in computational power improve computing efficiency; this enables SPH to simulate complex free surface flows. This dissertation studies the validation of SPH for free surface flow applications and explores a time series forecasting method to enhance CFD. There are four main contributions to this dissertation: First, we introduce artificial viscosity into SPH. In the meantime, this improved method is shown through demos of free surface flow in different cases. Second, based on the demos of the improved SPH method in Chapter 3, we discuss the water break models to explore the further application of SPH in complex coastal environments in Chapter 4. We also analyze the interaction between waves and various water break designs. Then, we assess the effects of different structures on wave overtopping to identify the optimal water break configuration. Third, we address the importance of water level research by examining water break models. In Chapter 5, with historical water level data from the Mississippi River, we discuss a time series analysis model based on ARIMA to forecast future water levels. The forecast results are in line with the actual trend. Finally, we show how GPU parallel computation dramatically improves the simulation efficiency of our model

Sleep, Emotion Regulation, and Relationship Satisfaction: A Moderation Analysis

Sleep plays a pivotal role in overall health and well-being, influencing mental health, cognitive abilities, memory, emotional balance, and physical well-being (Luyster et al., 2012; Mukherjee et al., 2015). Emotions constitute a natural and indispensable element of human existence but necessitate proper regulation, as any disruption in emotion control has been associated with various mental health disorders (Gross & Jazaieri, 2014; Kring & Gordon, 1998; Phelps, 2006). Furthermore, human connections and relationships are fundamental aspects of the human experience, contributing to emotional support, personal growth, a sense of belonging, and even influencing one\u27s physical health outcomes (Baumeister & Leary, 1995; Umberson & Montez, 2011). The interaction between these variables helps conceptualize the connection between our internal states, physiological states, and the quality of our relationships. Sleep plays a crucial role in regulating our emotions, affecting our mood, and influencing how we navigate interpersonal connections (Lemola et al., 2013; Weinberg et al., 2016). Additionally, emotional well-being has a profound impact on our ability to build and maintain satisfying relationships (Baglioni et al., 2010; Weinberg et al., 2016). The present study examined the interrelatedness of these concepts, examining emotion regulation as a moderator of the relationship between sleep and relationship satisfaction in a sample of 155 adults who were married or in long-term partnerships. Results indicated that both sleep and emotion regulation significantly predicted overall relationship satisfaction in the sample. Additionally, emotion regulation was found to significantly moderate the relationship between sleep and relationship satisfaction. Related implications, limitations, and directions for future research are addressed

The Moderating Effect of Empathy on the Relationship between Psychological Reactance and Intimate Partner Violence Perpetration

Intimate Partner Violence (IPV) is a phenomenon found across cultures, genders, and sexualities. Those who are victimized are more likely to experience negative health and psychological outcomes than those who have not and are also at an increased risk for becoming perpetrators themselves. This has resulted in a significant need to identify the risk factors associated with perpetrators, so cycles of violence can be broken and violence can be prevented. Previous research has utilized a predominately feminist perspective to understand IPV perpetration risk factors and has focused predominately on exclusively male samples. Additional research is needed to develop a broader understanding of the interactive processes that lead individuals to become IPV perpetrators. Using psychological reactance theory as a framework, the purpose of this study was to investigate if empathy moderates the relationship between psychological reactance and IPV perpetration. This study surveyed 278 participants recruited through Amazon’s Mechanical Turk (MTurk) survey platform. Results found verbal psychological reactance to be higher in those who did not endorse IPV perpetration and behavioral psychological reactance to be higher in participants who endorsed IPV perpetration. Empathy was not shown to be statistically significantly related to verbal or behavioral reactance and was also not found to moderate the relationship between reactance and IPV perpetration. Overall, the findings suggest that although there is a link between reactance and IPV perpetration, empathy does not strengthen or moderate the relationship between psychological reactance and IPV perpetration

DSA-API: Data Standardization Automation Using AI-Powered APIs

The common factor with current implementations of Artificial Intelligence (AI) is data. Companies are constantly looking for new ways to analyze data, but it comes in various formats: Text, Comma Separated Value (CVE), JavaScript Object Notation (JSON), Extensible Markup Language (XML), and Excel. How can AI be adapted to standardize formats for data analysis, integration, and digestion efficiently? Published research acknowledged that Machine Learning (ML) and AI can provide an automated method to speed up this process and limit the human decision-making error. With the advancement in AI, Application Programming Interfaces (APIs) prompt the idea that they can take in raw data as input into an AI platform and standardize the data for Large Language Model (LLM) algorithms. This research aims to standardize raw data using AI APIs, gather Open-Source Intelligence (OSINT) data to augment the raw data, and utilize the standardized data and OSINT for LLM ingest. Research results show that AI APIs can be used to standardize raw data by including various methods to reach this goal. This research demonstrated OSINT techniques to gather data and utilizing it for LLM algorithms. A LLM with ingesting a standardized dataset and OSINT data can be presented with specific questions to generate a response. In this research, the results depict a list of specific cyber-attacks that could be viable based off of the ingested data. Access to this information, when combined with knowledge of AI APIs and OSINT, provides an opportunity to create a process for standardizing raw data and exploiting the intelligence of LLMs