UARK (University of Arkansas )
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Enhancing The University of Arkansas\u27 Operations Through Data Science
Get PDFAs universities navigate financial constraints and resource allocation challenges, data driven financial analysis has become increasingly important. Universities employ various methods to assess financial efficiency, predict future expenditures, and optimize student credit hour distribution. However, the approaches to financial analysis vary widely, with some institutions leveraging advanced predictive modeling and business intelligence tools, while others rely on traditional budgeting techniques and manual forecasting.
This thesis examines how the University of Arkansas\u27 (“Uark”) financial analysis methods compare to those of other institutions and alternative data-driven approaches. Using four years of financial and student credit hour data, this study evaluates cost trends and student credit hour patterns in UArk’s financial management framework. Additionally, a comparative analysis is conducted to assess the strengths and limitations of different financial analysis methodologies.
Through this comparison, this research identifies best practices in data-driven financial planning and provides insights into how the University of Arkansas can improve its utilization of data science. The findings contribute to the ongoing discourse on data science applications in institutional decision-making, offering a framework for universities seeking to enhance their budgeting, forecasting, and resource allocation processes
Development of Modern Methods for Evaluation of Bridge Timber Pile Capacity
No full textTimber piles are widely used as foundational support for bridge substructures; however, they are susceptible to deterioration over time. This significantly impacts their load-bearing capacity and overall structural integrity. Despite their critical role in bridge safety, a lack of effective methods exist for assessing the in-service load capacity of timber piles. The ability to accurately determine load-bearing capacity is essential for transportation agencies to estimate the remaining service life of bridges, plan maintenance repairs, and ensure public safety. However, research in this area has been limited, with most studies focusing on retrofitting or repair techniques rather than evaluating the in-use structural capacity of timber piles. For this study, a survey was conducted to examine the current inspection methodologies used by bridge inspectors, the frequency of inspection, and the number of timber pile bridges found in various states throughout the country. The results indicate that many states rely on outdated inspection practices and lack standardized procedures for determining the load capacity of in-service timber piles. Furthermore, the survey revealed that most states do not calculate a timber pile load capacity rating but rather leave uncertainties regarding the actual weight limits that these piles can safely support. In addition to the survey, this research investigates the primary causes of timber pile deterioration, ARDOT’s current bridge rating procedures, and the tools presently used to assess timber piles. Timber pile testing and section loss analysis performed at the Civil Engineering Research and Education Center shows the reduced load capacity of a deteriorated pile. The overall objective of this study is implementing improved methodologies and load capacity calculations to enable transportation agencies to make better educated decisions regarding bridge maintenance, rehabilitation, and replacement, and therefore enhance the longevity and safety of timber pile-supported infrastructures
Childhood Housing Conditions as a Social Determinant of Health in College Students
Get PDFThis study investigates the relationship between childhood housing and current physical and behavioral health in Northwest Arkansas college students. Non-medical factors often determine a person’s health. Social determinants of health, as defined by the World Health Organization, are the conditions in which people are “...born, grow, work, live, and age...”, and they include wider systems such as economic and social policies, social norms, and political structures (World, 2023). A key social determinant of health is housing.
The average age of an undergraduate student at the University of Arkansas during the 2020-2021 school year was twenty-one (Gunderman, 2021). This is a critical developmental period where students prepare for adulthood, and poor physical health may be an obstacle. As a well-established social determinant of health, the housing a traditional college student grew up in may be playing a role in their current well-being and, with that, their future
Breaking ground: The impact of construction on nutrient loads in an urban stream
Get PDFDegradation of water quality is primarily driven by human activities, such as urbanization. Urban Stream Syndrome (USS) can be characterized by the negative physical, chemical, and biological changes to water quality as a result of urbanization. One key effect of USS is the input of excess nutrients, which can drive unwanted macrophyte and algal growth. New construction is prevalent in urbanizing areas, resulting in the influx of nutrients, sediments, and other materials into stream ecosystems. However, few studies have examined the impacts of active construction on water quality. As such, we do not understand how construction affects nutrient transport downstream. Here, we studied the impacts of construction on urban water quality in Tanglewood Branch in Fayetteville, Arkansas, which originates from a spring below an ongoing construction project. We measured soluble reactive phosphorus (SRP) and nitrate (NO3-) concentrations in water samples collected biweekly, and discharge in order to calculate loads at five sites downstream of the construction. We calculated instantaneous nutrient load and examined spatiotemporal variation. Preliminary findings show that SRP and NO3- loads decrease moving downstream of the construction, suggesting that in-stream biota are removing SRP and NO3- from the water column during transport. This study will increase our understanding of construction projects on water quality and assist in determining if municipalities may need to consider stricter construction regulations to restrict unwanted nutrients from entering surface waters.https://scholarworks.uark.edu/hnrcsturpc25/1043/thumbnail.jp
The Effect of Maternal Anxiety of Breastfeeding Exclusivity and Duration
Get PDFThis study examined whether postpartum anxiety affects breastfeeding exclusivity and duration among mothers in Northwest Arkansas. While maternal anxiety was not a significant predictor, child age and infant birth order were associated with breastfeeding outcomes.https://scholarworks.uark.edu/coesym25/1012/thumbnail.jp
Exploring Emotional Intelligence as a Determinant of Sales Performance: An Analysis using the Ability Model of Emotional Intelligence
Get PDFThis multi-phase research project explores the evolving role of emotional intelligence in the sales profession by focusing on four key themes: comprehending the intricacies of the sales field, understanding emotional intelligence, comparing emotional quotient to traditional sales tactics, and ultimately, harnessing emotional intelligence as a sales advantage. The study begins with an in-depth review of emotional intelligence theories and models, ultimately selecting the Ability Model as the framework for further analysis. Using this model, an emotional intelligence assessment was administered to students participating in the Walton College Sales Competition. Statistical analysis was then conducted to determine whether a correlation exists between higher emotional intelligence scores and stronger performance in the competition. The findings offer valuable insights into the potential impact of emotional intelligence on sales success and its implications for future sales training and recruitment strategies
Separating Signal From Noise in Annotator Disagreement
No full textNLP (natural language processing) models often rely on human-labeled data for training and evaluation. Many approaches crowdsource this data from a large number of annotators with varying skills, backgrounds, and motivations, resulting in conflicting annotations. These conflicts have traditionally been resolved by aggregation methods that assume disagreements are errors. Recent work has argued that for many tasks annotators may have genuine disagreements and that variation should be treated as signal rather than noise. However, limited work has combined the two frameworks to separate signal from noise in human-labeled data. In this work, we introduce NUTMEG, a new Bayesian model that incorporates information about annotator backgrounds to remove noisy annotations from human-labeled training data while preserving systematic disagreements. We then use a synthetic data evaluation framework to show that NUTMEG is more effective at recovering ground-truth from annotations with systematic disagreement than traditional aggregation methods. We provide further analysis characterizing how differences in subpopulation sizes, rates of disagreement, and rates of spam affect the performance of our model. Finally, we demonstrate that downstream models trained on data aggregated by NUTMEG significantly outperform both models trained on traditionally aggregated data and models trained on the full set of disaggregated annotations. Our results highlight the importance of accounting for both annotator competence and systematic disagreements when training on human-labeled data.https://scholarworks.uark.edu/hnrcsturpc25/1008/thumbnail.jp