Matching Items (433)
Description
The purpose of this thesis project is to analyze the impact that patient death has on long-term care providers. This study draws upon my own experience working as a licensed nursing assistant in a long-term care facility and also uses a qualitative analysis of six semi-structured interviews with other nursing assistants and hospice volunteers. With patient death being an unavoidable part of working in this area of healthcare, I explore how these care providers cope with losing their patients and the effectiveness of these coping mechanisms. Some strategies found that aided in coping with grief included staying detached from patients, being distracted by other aspects of the job, receiving support from co-workers, family members and/or supervisors, and having a religious outlook on what happens following death. In addition to these, I argue that care providers also utilize the unconscious defense mechanism of repression to avoid their feelings of grief and guilt. Repressing the grief and emotions that come along with patient death can protect the individual from additional pain in order for them to continue to do their difficult jobs. Being distracted by other patients also aids in the repression process by avoiding personal feelings temporarily. I also look into factors that have been found to affect the level of grief including the caregiver’s closeness to the patient, level of preparedness for the death, and first experience of losing a patient. Ultimately, I show that the common feelings accompanied by patient death (sadness, anger and stress) and the occurrence of burnout are harmful symptoms of the repression taking place.
ContributorsMasterson, Kaitlin (Author) / Loebenberg, Abby (Thesis director) / Mack, Robert (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
A desk provides an interesting forum between two people. The first party sits behind the desk while the second approaches with a question. The desk presents itself as a stage for the drama of that conversation to take place; as all furniture and property do, we naturally make assumptions about the owner based on the things they possess. Just as a Ferrari says one thing while a truck says something different, our furniture conveys a similar sensation. The desk is special because it acts as a stage - it can create a very subtle first impression of the person who owns it. The question then becomes, "what should I try to convey through the desk I sat behind?". If someone walked into my office and looked strictly at my desk, what impression would I want to give them about who I am as an individual? I conjunction with this question about the design of the desk itself comes to another question about the materials used. This thesis goes into the symbolic nature of wood in modern and ancient times across cultures, explores wood in modern construction today and explores the source of the wood used in this specific project through a supplier analysis of Porter Barn Wood. Porter Barn Wood is a local Phoenix company that specializes in reclaimed barn wood delivered from the east coast. Determining the story of how the wood got to Phoenix and to the company that made it possible was just as important to the story of the desk as the wood itself. Overall, this project explored my ability to construct a desk and build a story around that piece of art while maintaining a business mindset throughout. It was eye-opening to me and I would encourage you to read further!
ContributorsDuran, Alejandro Michael (Author) / Vitikas, Stanely (Thesis director) / Fleming, David (Committee member) / Economics Program in CLAS (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
I study some comparative statics implications of disappointment-averse preferences
for optimal portfolios. Specifically, I find that risk-averse disappointment-averse investors
increase investment in a risky asset as a result of a monotone likelihood ratio improvement in
the asset’s distribution, a subset of First Order Stochastic improvements. This gives a testable
implication between the disappointment aversion model, and alternatives, including expected
utility. I also discuss previously noted implications for disappointment aversion in helping
explain the equity premium puzzle.
ContributorsWarrier, Raghav (Author) / Schlee, Edward (Thesis director) / Almacen, Christopher (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
This project revolves around the enhancement of an existing data collection device utilized for patient monitoring within the framework of the leadership of Shad Roundy's team. The initial deployment involved a 10-Axis Internal Measurement Unit (IMU) sourced from MetaMotionS (MMS) for comprehensive data acquisition from patients at University of Utah’s Downtown Behavioral Health Clinic (BHC). The primary objective transitioned towards optimizing the device's functionality, particularly addressing challenges related to limited battery life, device size, and data transfer efficiency. A systematic approach was undertaken to address these challenges, involving meticulous research into alternative batteries, with the CL 582728 identified as a promising solution capable of extending the device's operational lifespan to around one month. Additionally, the initiative aimed at refining data collection processes through real-time transmission facilitated by Raspberry Pi devices at BHC via Bluetooth, leveraging the energy-efficient Nordic Semiconductor nRF52840 Bluetooth chip. The project also entailed intricate circuit design endeavors utilizing Autodesk Eagle, with reference to a model provided by MMS. Despite encountering programming challenges for the microcontroller, the groundwork was laid for a conceptual solution, with plans to delegate the programming task to a team member possessing advanced expertise. Though the device has yet to be fabricated, the design is near completion.
ContributorsJust, William (Author) / Andersen, Erik (Thesis director) / Roundy, Shad (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2024-05
Description
The rapid growth of published research has increased the time and energy researchers invest in
literature review to stay updated in their field. While existing research tools assist with organizing papers, providing basic summaries, and improving search, there is a need for an assistant that copilots researchers to drive innovation. In response, we introduce buff, a research assistant framework employing large language models to summarize papers, identify research gaps and trends, and recommend future directions based on semantic analysis of the literature landscape, Wikipedia, and the broader internet. We demo buff through a user-friendly chat interface, powered by a citation network encompassing over 5600 research papers, amounting to over 133 million tokens of textual information. buff utilizes a network structure to fetch and analyze factual scientific information semantically. By streamlining the literature review and scientific knowledge discovery process, buff empowers researchers to concentrate their efforts on pushing the boundaries of their fields, driving innovation, and optimizing the scientific research landscape.
ContributorsBalamurugan, Neha (Author) / Arani, Punit (Co-author) / LiKamWa, Robert (Thesis director) / Bhattacharjee, Amrita (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Economics Program in CLAS (Contributor)
Created2024-05
DescriptionThis project tracks the history of fiscal stimulus in the United States as it relates to defense and economic projects. This is done in order to place the Biden administration's fiscal agenda into a historical context of fiscal spending.
ContributorsMiller, Jordan (Author) / Calhoun, Craig (Thesis director) / Kirkpatrick, Jennet (Committee member) / Fong, Benjamin (Committee member) / Barrett, The Honors College (Contributor) / School of Politics and Global Studies (Contributor) / Economics Program in CLAS (Contributor)
Created2024-05
Description
The thesis explores the avenues of machine learning principles in object detection using TensorFlow 2 Object Detection API Libraries for implementation. Integrating object detection capabilities into ESP-32 cameras can enhance functionality in the capstone dragster application and potential applications, such as autonomous robots. The research implements the TensorFlow 2 Object Detection API, a widely used framework for training and deploying object detection models. By leveraging the pre-trained models available in the API, the system can detect a wide range of objects with high accuracy and speed. Fine-tuning these models using a custom dataset allows us to enhance their performance in detecting specific objects of interest. Experiments to identify strengths and weaknesses of each model's implementation before and after training using similar images were evaluated The thesis also explores the potential limitations and challenges of deploying object detection on real-time ESP-32 cameras, such as limited computational resources, costs, and power constraints. The results obtained from the experiments demonstrate the feasibility and effectiveness of implementing object detection on ESP-32 cameras using the TensorFlow2 Object Detection API. The system achieves satisfactory accuracy and real-time processing capabilities, making it suitable for various practical applications. Overall, this thesis provides a foundation for further advancements and optimizations in the integration of object detection capabilities into small, low-power devices such as ESP-32 cameras and a crossroad to explore its applicability for other image-capturing and processing devices in industrial, automotive, and defense sectors of industry.
ContributorsMani, Vinesh (Author) / Tsakalis, Konstantinos (Thesis director) / Jayasuriya, Suren (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2024-05
Description
For decades, society has held an innate fascination with serial murder and serial killers. The fascination lies in the motivations behind the actions and the way in which investigators apprehend them. The psychological field of investigative and behavioral psychology emerged to attempt to answer some of these questions and the investigative tool of behavioral profiling soon followed. Researchers have conducted comparison studies of male and female serial killers many times to understand what differentiates them. This research aims to answer another question: Are female serial killers more homogenous based on their profiles than male serial killers? The media portrays female serial killers in a very specific light, poisoners who kill due to revenge or money, but how well does this portrayal actually hold up when analytically examined? This research compiled case studies of fifteen male and fifteen female serial killers based on twenty-six characteristics and profiled each according to three different typologies to determine how homogenous these groups actually are. This research can help assist investigators and the public to better understand the diversity of these types of offenders and be able to determine who these offenders are.
ContributorsRotenberg, Taylor (Author) / Guyll, Max (Thesis director) / Madon, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Department of Marketing (Contributor) / Department of Finance (Contributor) / Economics Program in CLAS (Contributor)
Created2024-05
Description
Milk has long played an important role in American society and remains a popular staple of many Americans’ diets. Yet, despite its long standing popularity, the role of milk within American society has begun to develop new meaning in recent years. This paper aims to understand the symbolism that today’s Americans ascribe to milk. Academic journal articles, advertising campaigns, online articles, and government policy pertaining to milk were researched in order to identify the themes that characterize consumers’ perceptions of milk. In recognition of the diverse types of milk that are now accessible to many Americans, this paper uses the word “milk” to refer to cow-derived, fluid (liquid) dairy unless otherwise specified. This research reveals eleven principal themes that describe consumers’ perceptions of milk: milk symbolizes health, American values, is associated with athleticism, is unhealthy, is not preferable to plant-based alternatives, is bad for the environment, is animal cruelty, represents white supremacy, is anti-feminist, is reflective of consumer lifestyles, and there is a general trend of consumers being uninformed about the milk that they consume. This research helps to understand consumers; therefore, this research can be used to help dairy-related industries shape their business strategies and target their customer segment and to help policymakers design effective dairy-related policies. Furthermore, this paper invites further research to identify the consumers that hold the beliefs this research describes, and the extent to which these consumers share said beliefs.
ContributorsHladik, Jessica (Author) / Hughner, Renee (Thesis director) / Voorhees, Matthew (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / Economics Program in CLAS (Contributor) / Dean, W.P. Carey School of Business (Contributor) / School of International Letters and Cultures (Contributor)
Created2024-05
Description
This study presents a comparative analysis of machine learning models on their ability to determine match outcomes in the English Premier League (EPL), focusing on optimizing prediction accuracy. The research leverages a variety of models, including logistic regression, decision trees, random forests, gradient boosting machines, support vector machines, k-nearest neighbors, and extreme gradient boosting, to predict the outcomes of soccer matches in the EPL. Utilizing a comprehensive dataset from Kaggle, the study uses the Sport Result Prediction CRISP-DM framework for data preparation and model evaluation, comparing the accuracy, precision, recall, F1-score, ROC-AUC score, and confusion matrices of each model used in the study. The findings reveal that ensemble methods, notably Random Forest and Extreme Gradient Boosting, outperform other models in accuracy, highlighting their potential in sports analytics. This research contributes to the field of sports analytics by demonstrating the effectiveness of machine learning in sports outcome prediction, while also identifying the challenges and complexities inherent in predicting the outcomes of EPL matches. This research not only highlights the significance of ensemble learning techniques in handling sports data complexities but also opens avenues for future exploration into advanced machine learning and deep learning approaches for enhancing predictive accuracy in sports analytics.
ContributorsTashildar, Ninad (Author) / Osburn, Steven (Thesis director) / Simari, Gerardo (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05