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Alcohol and cognition: ego threat as a moderator
Get PDFThis study examined how ego threat (negative feedback) interacts with alcohol consumption to influence cognitive performance. We hypothesized ego threat would moderate the effects of alcohol on impulse control and attention in 30 college students (21-49 years). A 2×2 factorial design assessed performance on the Visual Search Task (attention) and Go/No-Go Task (impulse control) under laboratory and naturalistic settings, while sober or intoxicated. Alcohol consumption impaired performance, as evidenced by slower reaction times during visual search. An interaction effect emerged, with the combination of alcohol and ego threat further compromising reaction times. Interestingly, higher baseline impulsivity correlated with faster reaction times when sober. These findings corroborate prior research on alcohol\u27s negative impact on executive functions and suggest a potential moderating role of ego threat on higher-order cognitive processes, warranting further investigation
Parental childhood rejection: An exploration of anxiety and depression in later life
Get PDFMany studies have shown parental rejection can lead to depression. However, research exploring parental rejection and anxiety has been lacking and inconsistent, with some studies suggesting a relationship, while others do not. The current study aimed to examine if the perception of parental rejection in one’s childhood could predict trait anxiety and depression scores among young adults. Our hypothesis was that higher perception of parental rejection in childhood would predict both higher trait anxiety and depression. Study participants included 123 undergraduate students from a Southeastern U.S. university, with all participants being at least 18 years old. Correlations were explored across all variables. Consistent with past research, we found perceiving parental rejection at a young age correlates with higher depressive symptoms. However, no significant correlation was found between parental rejection and trait anxiety scores. Previous researchers suggest participants’ perceived lack of control and other non-parental factors may instead play a larger role in the development of anxiety and related symptoms. Limitations and future directions are discussed
Analysis and numerical simulation of tumor growth models
Get PDFIn this dissertation we focus on the numerical analysis of tumor growth models. Due to the difficulty of developing physically meaningful approximations of such models, we divide the main problem into more simple pieces of work that are addressed in the different chapters. First, in Chapter 2 we present a new upwind discontinuous Galerkin (DG) scheme for the convective Cahn–Hilliard model with degenerate mobility which preserves the pointwise bounds and prevents non-physical spurious oscillations. These ideas are based on a well-suited piecewise constant approximation of convection equations. The proposed numerical scheme is contrasted with other approaches in several numerical experiments. Afterwards, in Chapter 3, we extend the previous ideas to a mass-conservative, positive and energy-dissipative approximation of the Keller–Segel model for chemotaxis. Then we carry out several numerical tests in regimes of chemotactic collapse. These ideas are used later in Chapter 4 to develop a well-suited approximation of two different models related to chemotaxis: a generalization of the classical Keller–Segel model and a model of the neuroblast migration process to the olfactory bulb in rodents’ brains. Now we propose and study a phase-field tumor growth model in Chapter 5. Then, we develop an upwind DG scheme preserving the mass conservation, pointwise bounds and energy stability of the continuous model and we show both the good properties of the approximation and the qualitative behavior of the model in several numerical tests. Next, in Chapter 6, we present two new coupled and decoupled approximations of a Cahn–Hilliard–Navier–Stokes model with variable densities and degenerate mobility that preserve the physical properties of the model. Both approaches are compared in different computational tests including benchmark problems. Consequently, we propose, in Chapter 7, an extension of the previous tumor model including the effects of the surrounding fluid by means of a Cahn–Hilliard–Darcy model for which obtaining a physically meaningful approximation seems rather plausible using the previous ideas. Finally, this and other future lines of research are described, along with the conclusions and the scientific production of the dissertation, in Chapter 8
Understanding Taf13 (TATA box-binding protein-associated factor 13) upregulation in eukaryotic cells
Get PDFTATA-binding protein (TBP) and TBP-associated factors (Tafs) comprise RNA Polymerase II (RNA Pol II) pre-initiation complex. This universal component carefully controls the transcriptional initiation process. One of the Tafs, Taf13, also plays an important role in the regulation of RNA Pol II transcription initiation which is evolutionarily conserved from yeast to humans. It is found that Taf13 is overexpressed in cancer cells, although the exact mechanism that is responsible for this overexpression is unclear. Our hypothesis suggests that targeted degradation by the 26S proteasome via ubiquitylation [Ubiquitin-Proteasome System (UPS)] may be the mechanism that regulates the stability of Taf13. To test this possibility, we evaluated the role of UPS on the stability of Taf13 in yeast (Saccharomyces cerevisiae). Importantly for the first time, we found that Taf13 undergoes polyubiquitylation but it is not regulated by the 26S proteasome. These findings suggest further oncologic research topics for the development of therapeutic interventions for future patients of cancer
Fate and Treatability of Engineered Nanoparticles in Urban Stormwater
Get PDFDespite the tremendous benefits of engineered nanoparticles (ENPs), their inevitable release into the environment may be detrimental and hence cause for concern of these emerging contaminants. In this thesis research, the fate and transport of nano-silver (n-Ag) and nano-titanium dioxide in source specific stormwater is explored, and the influence of n-Ag on the treatability of co-pollutants is examined. Through the analysis of particle size distribution, it was noticed that metallic ENPs tend to aggregate initially, while extended turbulence in the system leads to disaggregation. Stormwater samples with added ENPs exhibited larger particles than those in stormwater without added ENPs. The effects of n-Ag were explored through adsorption batch tests of different media with aqueous solution of various copper and zinc concentrations and analyzed through atomic absorption spectrophotometry. There appeared to be a slight increase in both metal removal rates when n-Ag was present, although the difference was not statistically significant
Interactions between Anxiety, Family Influence, and Athletic Status on College Students: A Military School Cohort
Get PDFThe prevalence of anxiety in college students has increased drastically over the past decade. Previous researchers have typically only examined how to help students while they are at school, however, there could be additional factors contributing to their anxiety prior to attending college. The current study aimed to observe how family influence and athletic status may play a role in anxiety among college students. Participants (N = 42) completed an anxiety symptoms inventory then disclosed how much their family influenced their decision to attend college. A two-way ANOVA indicated that neither athletic status nor degree of family influence had a significant influence on anxiety symptom scores. However, participants who had moderate anxiety scores indicated that they attended the Virginia Military Institute on an athletic scholarship. Additionally, over half of all participants indicated that their family had influenced their decision to attend college. Gender did have a statistically significant effect on anxiety scores with female participants (t = -4.08, p \u3c .001) reporting higher scores. Based upon the findings of the current study, colleges should broaden their resources and cater services to each student, not to one specific group. Limitations and future directions are discussed
Harnessing Technology and AI to Unleash Workplace Potential, Enhance Performance, and Elevate Employee Workplace Experience
Get PDFTechnology, particularly AI, is becoming more prevalent in how work is accomplished. Businesses who understand this reality and invest in leveraging technology to enable employees to do their best work and achieve their highest potential. Proper integration of technology and AI can provide professional development and growth opportunities for employees and help businesses compete in the war for talent by becoming an employer of choice. This session will educate participants on technology workplace integration and explain the steps organizations can take for employees to benefit from this integration. The session will primarily focus on three benefits of technology integration to the employee experience: (1) efficiency in task completion allowing for additional work enrichment; (2) reduced stress and frustration due to improved consistency of workflow; and (3) enhanced job performance. The session will illustrate these benefits through interactive scenarios that allow the audience to gain first-hand experience of the value of technology, AI, and the algorithms that underlie them. With the benefits of integrating new technology into the work experience, it is also crucial to recognize that with this novelty comes resistance from employees due to the established procedures, norms, and operations within an organization\u27s culture. This resistance, in part, comes from employee algorithm aversion, which is the tendency to neglect algorithmic decisions in favor of one’s own decisions despite algorithms\u27 demonstrated superior performance. Algorithm aversion represents a significant barrier to the benefits of technology integration. Businesses need to understand these barriers and develop training, development, and employee support mechanisms to successfully level technology and enable employees to do their best work. This session will focus on five specific barriers: (1) algorithms illiteracy, (2) uncertainty around how to make decisions with algorithmic support, (3) lack of appreciation of human and algorithms decision accuracy, (4) discomfort with AI and algorithms making ethical decisions, and (5) uneasiness from exclusively receiving feedback from technology. This session will also discuss how organizations can support their employees through the integration process. More specifically, the session will explore how to (1) implement algorithms literacy training, (2) use storytelling to open the “black-box” nature of algorithms, (3) blend AI and employees decisions by empowering input modification, and (4) develop interactive processes with human touch into technological generated feedback. Attendees will gain insights on how their organization can ensure employees gain value from technology integration and manage the new culture these technologies create through this interactive session
Understanding Boreout: A New Measure of Employee Well-Being
Get PDFBoreout, the opposite of burnout, arises when employees feel disengaged, lack meaning in their work, or face underutilization. This can harm both employee well-being and organizational outcomes, such as performance and retention. To better assess boreout, we developed a new scale based on existing measures, focusing on two factors: Workplace Boredom and Underutilization. Data from 150 participants showed high reliability for both factors, contributing to the understanding of boreout and offering a validated tool for future research
Aiming for Success with DART: Determining Data Analytics Readiness for Targeted Results
Get PDFIntroduction Organizations are interested in finding ways to cut down on lost productivity while simultaneously minimizing injuries to employees. The private sector of the workforce accounts for over 2.8 million illnesses and injuries in the U.S. (BLS, 2018, 2019, 2020). The Data Analytics Readiness Tool (DART), was created to ascertain an organization’s advanced analytical capabilities on whether their data can be used to perform descriptive, diagnostic, predictive, and prescriptive analytics. For organizations to take further proactive steps for employee safety and avoid injuries before they occur, the DART\u27s feedback is essential for improving their safety measurement systems to use additional predictive analytic approaches. Methodology The DART is a self-assessment tool used to measure data maturity and analytic capabilities (Leslie et al., 2024). DART was applied to two years of data (2022-2023) provided by a large oil refinery in the American Southwest. The data examined consisted of behavioral observations, safety audits, and injury data ranging from close-calls and first-aids, to more severe incidents including injuries requiring long-term treatment. HR-related examinations consisted of overtime and scheduled/unscheduled hours. These results were aggregated providing “readiness” scores determining which variables can be in predictive analyses (Leslie et al., 2024). Scores above 75% were optimal, while scores below 50% were suboptimal. Results The organizational and variable readiness scores mostly exceeded optimal levels (Leslie et al. 2024). The readiness scores inferred from those scores were personnel, centralized database, employee participation, and management use with all but centralized databases being optimal. The variable readiness scores of safety-related and HR-related variables all exceeded the optimal level (Leslie et al., 2024). Thus, the safety-related and HR data received, passed the DART criteria and can be used for further predictive analyses. When compared to previous applications of the DART, this organization exceeded the overall optimal levels of two other companies. Conclusion Using the DART has allowed for the ability to assess and implement safe work practices (Ezerins et al., 2022). Even though some of the variables did not meet the 75% threshold for predictive analytics, the overall findings can still provide insight into whether an organization is ready for predictive analytics to provide beneficial results. These results show the benefits that a self-assessment tool can provide not only within a safety context but also for broader organizational practices. Organizations can leverage predictive analytics to create dynamic employee value propositions by anticipating the needs and expectations of their employees
Investigating job attitudes of justice-involved individuals
Get PDFThe US has one of the highest incarceration rates in the world resulting in one in three Americans having a criminal record (ACLU, 2017). Upon release from prison, securing employment is one of the most important factors to decrease recidivism (Berg & Huebner, 2011; Lundquist et al., 2018). Despite this, 75% of those who were incarcerated are unemployed a year after release. This is due, in part, to stigma and the wide use of background checks during the hiring process (ACLU, 2017). The research literature primarily focuses on effective ways to improve job-readiness for those who are incarcerated, barriers to employment for justice-involved individuals (JII), and other risk factors and mental health issues related to incarceration (Griffith et al., 2019). Few studies have investigated what happens once a JII gains employment. We will test the claim made by the American Civil Liberties Union (2017) that, if given a second chance, JIIs will make loyal and hardworking employees. To do this, we will measure two important and well-understood job attitudes with a survey: turnover intentions and organizational commitment. We will then conduct a t-test (and follow-up ANCOVA if necessary) to compare justice-involved employees to other employees to see if there are any differences in these attitudes. To recruit participants, we will establish partnerships with businesses who have a reputation for employing this population and city officials who work with this population. Our hypotheses are the following: (H1) Organizational commitment and turnover intentions will have a significant negative relationship. Individuals with a criminal background will be motivated by their stigmatized identity to be more committed to their organization while simultaneously perceiving fewer alternative job opportunities. (H2) JIIs will have a significant difference in organizational commitment and turnover intentions compared to other employees. (H2a) Organizational commitment will be greater in JIIs than in other employees. (H2b) JIIs will have lower turnover intentions than other employees. (H3) Criminal background will moderate the relationship between organizational commitment and turnover intentions, such that JIIs will have a stronger relationship between organizational commitment and turnover intentions. In addition to these hypotheses, we will explore possible differences in commitment mindsets and utilize open-ended questions to learn more about the job attitudes and work-related experiences of JIIs. We hope our findings will help improve job prospects for JIIs by reducing the stigma of employing these individuals. These findings may help managers better support JIIs in the workplace as well