bluuebunny/crossref_metadata_2025 huggingface dataset:
Increasing caregiver mental health literacy to support middle school youth : a randomized control trial of a brief virtual intervention
Authors:   , Denhof Amanda
Published: November 2024

Effective mental health literacy intervention for caregivers is a relatively under researched and underdeveloped field in psychology (Hurley et al., 2020). The National Alliance for Mental Illness (NAMI) identified that 1 in 6 U.S. youth aged 12-17 experience a major depressive episode, making middle school or grades 6-8 a critical period for early intervention to address emerging depressive episodes (NAMI, 2022). Youth mental health disorders are associated with numerous poor school and life outcomes including suicidal ideation and attempts, academic underachievement and school dropout, substance use and disorders, and physical fighting or victimization by a weapon (Dever et al., 2013; Lee et al., 2017). Providers and researchers have identified the importance of family centered care and the role that caregivers play in youth accessing and receiving treatment for mental illness. Mental health literacy (MHL) is defined as the knowledge, attitudes, and beliefs about mental health disorders, helpseeking, and treatment that aid in the recognition, management, and prevention of such disorders (Jorm et al., 1997). The objective of this study is to develop a brief intervention for caregivers and evaluate its impact on mental health literacy, depression literacy, and anxiety literacy for youth mental illness. Pre- and post- data using the Mental Health Literacy Scale, the Anxiety Literacy Questionnaire, and the Depression Literacy Questionnaire will measure changes in caregiver MHL for treatment and control group participants.
Leading in an era of school violence : a qualitative study of the impact of school shootings on school principals
Authors:   , Dubei Jaime Anne
Published: November 2024

This case study explored the impacts of school shootings on K-12 school principals. Eleven school principals and assistant principals from rural, suburban, and urban areas in all regions of the United States were interviewed. The study answered two research questions: What impact do school shootings have on school principals? and What do principals recommend to create a climate of positive support for principals related to school shootings? Guided by Cognitive Behavior Theory, findings describe the emotional toll of school shootings, including their thoughts, feelings, and behaviors. Findings include the impact of immediate news of school shootings via social media. Principals made recommendations, including high-quality district support, upgrades to the physical building, professional learning, networking, and political change, to prevent or reduce the frequency of school shootings. High-quality district support includes increased concern for principals and staff and coordinated and prioritized initiatives. Upgrades to the physical building included safety installations (e.g., door alarms, cameras, scanning and/or secure entry vestibules). Professional learning needs included opportunities for principal networking to plan for emergent situations, and ongoing networks of support. Lastly, principals requested political change to reduce or eliminate the frequency and intensity of school shooting incidents. This study contains principals' voices on this topic, which is not prevalent in the current body of research.
Advanced analytical and machine learning methods for analysis of selection and prediction of mortality in commercial swine
Authors:   , Grohmann Caleb Jerome
Published: November 2024

The age of information and the Internet of Things (IoT) has brought forth many exciting opportunities for farmers and researchers in commercial swine production. The amount of data available across all sectors of the industry is rapidly increasing, which requires innovative methods to store, analyze, and derive insights to positively impact producer economic sustainability. In chapter one of this dissertation, we propose a framework for shifting from reactive to proactive livestock management, which is assisted by technologies in artificial intelligence. Further, in chapter two, a novel method known as generation proxy selection mapping (GPSM) was utilized to identify single nucleotide polymorphisms in a commercial pig population that are undergoing significant changes in allele frequency over short time scales (i.e., four to ten years). In chapter three, we developed an algorithm to identify periods of episodic mortality in commercial wean-to-finish pig cohorts, which reveal sequences of days in which mortality is acutely increased relative to population baseline. Lastly, in chapter four, we evaluated various machine learning models to forecast episodic and sporadic mortality in growing pigs. Results from this work can promote evidence-based, data-driven decision making in commercial pig production in real-time.
Exploring organizational pressures on data driven decision making in social service nonprofits : a Grounded Theory study
Authors:   , Hammock Kasey Lynn
Published: November 2024

In most sectors across the United States, individuals within organizations use data-driven decision making to understand their outcomes and make decisions about the future of their organization's work (Brynjolfsson et al., 2011). This is rarely happening in the approximately 112,000 human service nonprofits in the United States (Bopp et al., 2017; National Center for Charitable Statistics [NCCS], 2020). Recognizing these barriers is crucial for progress. Data-driven decision making (DDDM) involves three stages: defining relevant data, establishing measurement protocols, and utilizing data for decision-making (Julnes and Holzer, 2001; Mandinach, 2012). The study seeks to build a theoretical foundation lacking in previous literature by interviewing leaders and front-line workers at social service nonprofit organizations in Boone County, Missouri to better understand influences on their utilization of data in decision making. Employing a non-experimental Grounded Theory approach, the study explores the relationship between organizational state and actions within social service nonprofits, aspects of DDDM. Qualitative semi-structured interviews and quantitative demographic data collection were used to develop a theoretical model for DDDM in this sector. Twenty organizations were randomly selected from a sample frame of 55 social service nonprofits, resulting in 34 participants from 18 nonprofits. Qualitative data, gathered through interviews, provided insights into participants' experiences and perceptions of DDDM effectiveness. A Grounded Theory analysis was employed to analyze interview transcripts. Quantitative data, including organizational information from IRS reports and individual self-report surveys, supplemented the qualitative findings, allowing for robust validation of qualitative results. The study revealed three key findings: 1) the differing influences of internal and external stakeholders on organizational actions; 2) the significant role of organizational capacity in shaping data definitions and processing; 3) and the primacy of organizational factors over individual ones in influencing DDDM. These findings lay a foundation for future research and practical interventions aimed at fostering effective DDDM in social service nonprofits. By addressing gaps in current literature and offering insights into challenges and opportunities for DDDM implementation, this research contributes to the advancement of effective, equitable, and sustainable practices within social service nonprofits.
High-needs, predominantly Black schools : an analysis of staying teachers
Authors:   , Harris Julie Finnie
Published: November 2024

The purpose of this study was to explore the factors that motivate effective teachers to remain in their high-needs, predominantly Black schools. Data shows that schools enrolling majority populations of low-income students of color experience high teacher attrition rates, which contribute to scarcities in the workforce. These scarcities often lead schools and districts to place inexperienced, unprepared, or uncredentialed teachers into classrooms, thereby compromising student learning and achievement (Garcia & Weiss, 2019a; Garcia & Weiss, 2020; Sutcher et al., 2016.) While attrition and shortages plague many high-needs schools, there exist effective and qualified teachers in applicable schools who are deeply committed both to their school communities and to the students served within those communities. This investigation sought to explore the motivations of the "staying" teachers by examining one fundamental question: What internal and external factors motivate effective teachers to remain in high-needs, predominantly Black schools? This study was positioned within two charter-public elementary schools in a major metropolitan city in the Midwest and explored the experiences of five teachers, each of whom had remained in their current high-needs, predominantly Black school for at least three years. Using a basic interview design, the study examined factors that motivate teacher retention in applicable schools by engaging study participants in a focus group and individual interviews. Findings from the study show that effective teachers who choose to remain in their highneeds predominantly Black schools are motivated by support from their administrative teams and by a sincere love for children who live in the community served by the school. The findings also indicate that staying teachers consistently demonstrate human agency and high-self-efficacy behaviors, which consistently drive their efforts to create and promote school-level, student experiences that have an enduring impact on student development and achievement.
Professional learning committees impact on student achievement in schools using standard referenced grading practices
Authors:   , Patton Catherine Irene
Published: November 2024

Educational reforms have occurred through the centuries in education and while some are fleeting, others find themselves as anchors in education. The main goal of all educational reforms typically evolves around the desire for students to achieve academic success. This research aims to promote a better understanding of how the blending of two educational models, standard referenced grading (SRG) and professional learning communities (PLCs) can contribute to a greater increase in student academic achievement and examine how teachers perceive the impact PLCs have on student achievement when implementing SRG in an English Language Arts course. The research also helps to promote that when these models are implemented with fidelity, the result of teacher and student growth is evident in student achievement. In this qualitative research, data was collected through interviews of 13 English Language Art teachers from three schools in the same district and student assessment data. Results from this research found that teachers perceived an increase in student academic achievement when participating in professional learning teams (PLTs) in schools using SRG when effective communication was evident.
Understanding how school resource officers construct their identity in a school
Authors:   , Ronnfeldt Adam J
Published: November 2024

School Resource Officers (SRO) are a vital part of any school district and school building. This research project aims to understand how SROs construct their identity in a school building. How do SRO go about in creating their role with all stakeholders of the school community through the frame of Role Identity Theory. A single case study was used for the research design. The data for this project was collected through interviews, observations, and documentation of 4 school SROs, 7 security advisors, and 3 high school principals. The themes that emerged from the research included relationship building and trust, communication and collaboration, and following the school districts Memorandum of Understanding (MOU). The themes from the study will help improve processes and procedures for SROs and school administrators. The research will help to further expand the knowledge of SRO perspectives and further future research with specific strategies and techniques that SROs use to build their role within a school building.
Education stakeholder perceptions of public funding in private schools in one rural Missouri school district
Authors:   , Shields Mary
Published: November 2024

School choice in education has been a hot-button topic in the United States for hundreds of years (Chen, 2024). This qualitative, bounded case study conveys the history of education including the changing landscape of school options. Private and parochial school stakeholders were asked their perception of using public funds for private/parochial school education. One community housing three school choices is the setting for the study which led to three definite themes; support for private/parochial schools, support for public schools, and support for using public funding for private parochial schools.
The party brand and economic voting
Authors:   , Shin Victor Tae Hyak
Published: November 2024

Economic voting is both a normative prescription of democratic theory and a heuristic that voters use to make inferences about the government. While the model simplifies many of the requirements for accountability, it also strips voters and political parties of ideological preferences and goals. I identified a few instances within the literature on the clarity of responsibility that are inconsistent with the assumptions of the retrospective model of economic voting. These instances suggest that voters are prospectively and ideologically oriented when voting for a party. I offer an alternative explanation for these results based on the ideological brands that parties develop. In sum, I argue that economic voting effect will be stronger when voters can clearly perceive an ideological distinction between the government and the opposition. First, I provide evidence that intra-party cohesion and party behavior affect the uncertainty associated with a party's ideological brand. Next, I introduce the concept of ideological distinctiveness and provide evidence that uncertainty interferes with one of the main functions of ideologies, which is to provide a means to compare and distinguish between party programs. Finally, I argue that uncertainty and ideological distinctiveness between government members and the opposition affect voters' capacity to cast an economic vote. Specifically, I argue that voters dissatisfied with the economy look for an ideological alternative to the government, and when a clear ideological alternative is found in the opposition, economic voting tends to be strong.
Leading change in American international schools in Colombia : contextual challenges and strategies for success
Authors:   , Shirk Timothy Alan
Published: November 2024

This dissertation explores the contextual challenges leaders face in American International Schools in Colombia (AISC) and how that context affects their approaches to leadership. It looks at the dynamics of leading change within the AISC context, seeking to understand the challenges leaders face when leading change and the strategies they use to overcome them. In the diverse contexts of AISC, leaders confront various cultural and contextual intricacies that make for a unique and challenging landscape in which to lead. Utilizing a qualitative case study approach, leaders of AISC were interviewed to uncover the challenges they encounter and the strategies they use to lead change. Additionally, this research explains how the cultural and organizational settings within which AISC leaders operate shape their leadership decisions. This study contributes to the existing literature on leadership and leading change in AISC while providing leaders with a resource to lead more effectively within these uniquely challenging educational environments.
Greenhouse gas emissions reduction policies and trade, cropland responses to incentives, rice farmer efficiency
Authors:   , Woo Sunghwi
Published: November 2024

The first essay examines the effects of potential EU and USA greenhouse gas emissions (GHG) policies that are unilateral or bilateral and with or without export exemptions. An existing partial equilibrium model of the global agricultural market, called Aglink-Cosimo, is employed to conduct a simulation analysis. Major results are as follows. First, unilateral or bilateral GHG policies tend to increase prices in the domestic and world markets and decrease production and trade quantity, although the effects vary depending on commodity and country. Also, moving from a unilateral to a bilateral policy can cause significant trade diversion but does not have a large impact on the domestic market. Finally, GHG policies can induce carbon leakage, and how border adjustments are designed is important to mitigate carbon leakage. The second essay estimates the area elasticities for 10 countries or regions for most crops using the Bayesian methods to ensure that the results are consistent with the expectations suggested by the economic theory and applied economics. The results show that the area responses are different depending on crops and countries or regions. This study also tests partial adjustment and limitations on the total area responsiveness. As a result, including lagged area as an independent variable affects some crop's area response. Also, setting an upper bound has a large impact on the United States area responses, but does not on Brazil. These results imply that it is proper to use different area elasticities depending on the form and structure of global crop models. The third study estimates the technical and environmental inefficiency of Korean rice farms and relates to potentially competing societal objectives to maximize food production or to minimize greenhouse gas (GHG) emissions. The results show that putting all priority on output efficiency, the expected practice of a profit-maximizing firm, increases GHG emissions. If instead environmental efficiency is the sole focus, then some farms do not need to reduce rice production to reduce GHG emissions. However, more than half of the farms in the sample experience a decrease in rice production in this case. The exact impacts are found to depend on social-environmental variables such as age and regional dummies. We go farther and estimate each farm's relative prioritization of these two outcomes based on their actual choices. Farms are found to put more weight on rice production than the environmental outcome, although the move of the weight from economic to environmental outcomes might lead to a reduction of GHG emissions without a large economic loss. However, farms located in urban areas place a significantly higher weight on environmental outcomes than farms in other regions.
Teachers' perceptions of the benefits and effectiveness of SEL in elementary schools : a qualitative study
Authors:   , Young Treaka Nicole
Published: November 2024

Emotional struggles among students have long been a challenge in the educational sector. This study aimed to investigate the implementation and effectiveness of social-emotional learning (SEL) programs in elementary schools, specifically focusing on the impact of SEL among Black male youths. This case study revealed that counselors and teachers perceive SEL as beneficial for students in managing emotions, resolving peer conflicts, and developing healthy relationships. Furthermore, the study suggests the need for future research to explore the potential positive impact of expanding the SEL curriculum beyond specific regions to understand how SEL skills acquired in elementary school can influence student late in life, including during their college years. Additionally, it emphasizes the necessity of comprehensively understanding the correlation between social-emotional development and absenteeism and analyzing data by gender, race, and age groups. Finally, the study recommends further research to track graduation rates, in-school suspensions, Individualized Education Program (IEP) utilization, and future student outcomes of interest.
Building an extensible, AI-augmented ecological momentary assessment platform
Authors:   , Zhao Fengxiang
Published: November 2024

In the rapidly evolving landscape of data-driven research, mobile ecological momentary assessment (EMA) has emerged as a powerful tool for real-time data collection. TigerAware, an advanced system designed for the user-friendly creation and deployment of surveys on mobile devices, has undergone significant enhancements to become a multi-faceted platform. This thesis presents several innovations that leverage advancements in large language models (LLMs) and foundation models to augment TigerAware's capabilities and advance the field of mobile EMA. We developed novel methods for automating the qualitative analysis of unstructured text responses collected through EMA. Our LLM-driven approach, which includes a Keypoints Extractor module and a Keypoints Relevance Evaluator, demonstrated superior performance in extracting concise, relevant keywords and accurately linking free text arguments to their pertinent points. Additionally, we introduced Argument2Code (A2C), a method that utilizes LLMs to automate inductive coding in qualitative data analysis by generating comprehensive codebooks. These advancements significantly enhance the automation and sophistication of the coding process in qualitative research. Furthermore, we proposed FENAP (Foundation Models for Comprehensive Analysis and Prediction of EMA-derived Narratives), which applies the capabilities of foundation models to interpret and analyze EMA data. By transforming raw EMA inputs into structured narratives, FENAP captures detailed behavioral patterns and dynamics, enabling a deeper understanding of human behavior. We implemented this methodology using the diverse UT1000 Project dataset and demonstrated that FENAP outperforms traditional and advanced machine learning baselines in predicting behavioral outcomes. To address critical challenges in privacy protection within EMA, we introduced a novel dataset and a hybrid AI approach that combines DistilBERT and LLaMA models for detecting privacy-sensitive information (PSI). This approach surpassed existing baselines in PSI detection accuracy and provided clearer explanations for model decisions, advancing privacy protection methods in EMA. In addition to these contributions, we made significant enhancements to the user experience, workflow optimization, and software engineering aspects of TigerAware. These improvements include interface refinements, streamlined workflow tools, enhanced communication features, and the incorporation of strong software engineering standards, ensuring TigerAware's reliability, extensibility, and user-friendliness for cutting-edge research. Our contributions underscore TigerAware's evolution into a dynamic and comprehensive EMA platform, ideal for the demands of various research domains. The integration of modern AI technologies, such as LLMs and foundation models, into real-world data collection and analysis platforms demonstrates the immense potential for advancing research in the field of mobile EMA.
Interpreting the narratives and themes in the memorials and markers commemorating the Santa Fe Trail
Authors:   , Terhune Michael
Published: November 2024

Between March and July 2021, I traveled the routes of the Santa Fe Trail from Franklin, Missouri, to Santa Fe, New Mexico, documenting the memorials and markers placed to commemorate the trail's use from 1821 until 1880. Photographs of the markers and memorials were taken, and their locations were recorded by GPS. Finally, written information on the memorials and historical markers was extracted to identify the common themes visible to tourists. Three major themes emerged: Physical Landscape, Economics, and Government Involvement. The physical landscape theme documents physical features that affected the trail's routing, traversing, and hardships endured. The economics theme relates to the trail being primarily used for commercial trading and the industries founded to support the logistics of trail movement. Government involvement describes military and regulatory efforts that aided in conducting trade operations with a foreign power and interactions with indigenous peoples. Each theme will be briefly explored with historical context and the implications of their significance for remembering the past along the Santa Fe Trail.
Smart strategies for building energy efficiency : integrating occupancy-based HVAC control and machine learning predictions
Authors:   , Uddin Meftah
Published: November 2024

In the realm of commercial building energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) systems play a pivotal role, around the globe. However, inefficiencies in HVAC control practices lead to significant energy wastage through practices like overcooling, overheating, and running HVAC systems in unoccupied spaces. Compounded by fixed HVAC scheduling due to limited occupancy data, this norm exacerbates energy overuse and ecological impact. This study addresses the challenge through occupancy-based HVAC control strategies, bolstered by machine learning predictions. The study delves into using occupancy insights for HVAC control, utilizing simulations to uncover potential energy savings. It extends its reach into time series forecasting, predicting energy patterns for short terms. This proactive approach empowers HVAC systems to optimize schedules, curbing wasteful consumption and enhancing overall efficiency. This integration of occupancy-based strategies and predictive modeling emerges as a pioneering framework to reduce energy waste in commercial buildings. By coupling real-time occupancy insights with advanced modeling, the research not only reveals untapped energy conservation potential but also charts a path toward sustainable and efficient energy management. In an era of mounting energy efficiency focus, this study promises economic and environmental gains in the commercial building sector.
A data-driven approach to evaluating the effectiveness and adverse outcomes of antidepressant exposure using longitudinal real-world EHR data
Authors:   , Alaboud Khuder Ali
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] This dissertation explores the complex dynamics of treatment outcomes and body weight changes in patients undergoing antidepressant therapy, employing advanced machine learning techniques to model and predict clinically significant events. Depression remains one of the most pervasive and challenging mental health disorders worldwide, significantly impacting the quality of life and well-being of millions. The variability in individual responses to antidepressant treatments necessitates a data-driven approach to improve predictive models and optimize therapeutic strategies. Leveraging a robust dataset from the PCORnet Common Data Model, which encompasses comprehensive electronic health records (EHR) of over 2.25 million patients, this dissertation employs advanced machine learning techniques, specifically Bidirectional Long Short-Term Memory (Bi-LSTM) with an attention mechanism, to capture the temporal dynamics and complexities inherent in longitudinal EHR data. These models predict diverse treatment failure outcomes, categorized by clinical severity from dosage adjustments to critical events like hospital admissions and mortality, and assess the impact of antidepressants on body weight, a critical aspect of treatment affecting patient health outcomes. Integral to this research is the commitment to model transparency and explainability, achieved through the integration of SHAP (SHapley Additive exPlanations), which enhances the interpretability of the models for clinical practitioners by detailing how patient characteristics and treatment variables contribute to model decisions. The findings suggest that personalized treatment plans based on predictive analytics can substantially improve patient outcomes, advancing the field of health informatics by demonstrating the application of machine learning in understanding and predicting the outcomes of antidepressant treatments and setting a precedent for future research in personalized medicine approaches in psychiatry.
Natural resource justice as an anticolonial practice : policies of sovereignty
Authors:   , Carter Samantha Joe
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] Natural resources underpin our wellbeing as a species. Humans attribute value to these resources in different and sometimes competing ways. For Indigenous peoples, government dismissal of their natural resource values has created an unequitable relationship, where Indigenous social, political, economic, and cultural ties to nature are damaged. The shared struggle for Indigenous people living in post-colonial governments is that the pervasive effects of settler colonialism are ongoing, and the pursuit for natural resource rights continues. This dissertation uses legal document analysis and unstructured interviews with key informants to examine governance policies and structures through the lens of Indigenous rights. The dissertation is formatted in three manuscripts. The first manuscript examines jurisdictional tensions over surface mining rights on tribal lands in the state of Oklahoma following the United States Supreme Court case McGirt v. Oklahoma (2020). The second manuscripts examines the implications of changing federal Cannabis policy for the enforcement of criminal law, regulation of commercial activity, and regulation of cannabis research in Indian Country. The final manuscript identifies challenges in multilevel governance of natural resources with Indigenous peoples based on the perceptions of key informants in Aotearoa New Zealand and Hawaii. Overall findings reveal natural resource management challenges specific to Indigenous populations are complicated by legal pluralism, cultural mismatch, and institutional voids, and action must be taken by post-colonial governments to uphold the land and natural rights guaranteed to Indigenous peoples.
Neural oscillations : two characterization algorithms and a biophysical network model
Authors:   , Chen Ziao
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] Neural data has become increasingly accessible due to advances in recording techniques and computational capabilities. This abundance of neural data presents both a significant challenge and an essential endeavor: the systematic analysis of this data and the application of reverse engineering principles to reveal the brain's concealed mechanisms. Computational neuroscience plays a key role in this effort, aided by the rapid progress in statistical techniques and machine learning. Within this context, neural oscillations are of great importance. They coordinate neuron ensembles, facilitating communication and cognitive processes. This dissertation focuses on the analysis of electrophysiological data and computational models at the cellular and network levels. The three core chapters aim to address the following objectives: In chapter 2, a pipeline was developed to improve characterization of oscillatory bursts and synthesize surrogate data that facilitates the evaluation of detection algorithms from individual recordings. In chapter 3, a scheme using simulations and machine learning was developed to infer the properties of single neurons from in vivo extracellular recordings, enhancing the construction of realistic network models. In chapter 4, a biophysical realistic computational model was used to investigate how microcircuits in the motor cortex interact to generate beta and gamma oscillations.
Family impacts of diabetic extremity wounds : a qualitative case study
Authors:   , Doss Elizabeth
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] One in every three persons with diabetes mellitus will develop a diabetic extremity wound as a complication of diabetes within their lifetime. Diabetic wounds and subsequent amputations can have significant adverse consequences which affect not only patients, but also the family members who care for them. Informal family caregivers perform many vital health assistance tasks over months or even years of chronic wound treatment. Little previous research on family-wide experiences of diabetic extremity wounds has been performed in the United States. This qualitative dissertation used a multiple case study design to explore the experiences of families caring for a member with a diabetic extremity wound. Themes related to impacts on family functioning, the caregiving experience, and formal healthcare utilization were identified. These findings provide insight into the experiences of families living with diabetic extremity wounds, as well as clinical and research directions for the future.
Micellar catalysis enabling sustainable synthesis
Authors:   , Kaur Gaganpreet
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] The concept of micellar catalysis (Chemistry in water) and its applications in the development of various sustainable methodologies are discussed in this dissertation. It also discusses the synthesis of rationally designed amphiphiles and their multiple functions for sustainable synthesis. These novel methodologies reduce the reliance on expensive ligands and toxic organic solvents compared to traditional methods. Sustainable methodologies for the synthesis of valuable organic compounds, such as unsymmetrical biaryl ketones, and functionalized indoles, have been developed. In addition to this, a methodology for the functionalization of styrenes has been developed under aqueous conditions. Chapter 1 reviews the concept of Micellar catalysis (a reaction in water) and analytical techniques for probing the mechanism of reactions in a micellar environment. Chapter 2 discusses the development of a ligand-free protocol for the synthesis of asymmetrical biaryl ketones. The current methodologies suffer from the use of expensive multistep synthesized ligands and toxic organic solvents. With the developed methodology, biaryl ketones were developed without the use of ligands in aqueous conditions. Moreover, the new methodology is easy to execute. Chapter 3 highlights the synthesis of a novel surfactant for ligand-free gold-catalyzed synthesis of functionalized indoles. The methodology is easy to execute and does not require the use of toxic organic solvents. A wide variety of functionalized indoles were obtained, including the complex molecules from the Merck informer library. Chapter 4 discusses the sustainable protocol for the functionalization of styrenes with azides and carboxylic acids under aqueous micellar conditions using our rationally designed PS-750-M as an amphiphile. Under the shielding effect of micelles, styrenes, and azides can be used safely, even if they proceed through radical formation.
"We can't be paralyzed by fear" assessing risk in local television news : an exploratory study on management strategies and decision making
Authors:   , Khanom Asma
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] The concept of risk encompasses a wide range of potential negative outcomes, including business losses, audience attrition, credibility challenges, and other journalistic issues. This study investigates the risk factors associated with economic downturns, technological advancements, and organizational changes in local TV news stations. By examining how management perceives and evaluates risk, this study aims to contribute to the boundary work theoretical framework, which illustrates how industry dynamics influence institutional behavior within local television stations. Nineteen mid-level news managers from Missouri's major television markets, including St. Louis, Kansas City, Springfield, and Columbia-Jefferson City, participated in interviews for this study. The findings indicate that economic conditions heavily influence management decisions. While mid-level leaders have limited decision-making authority, they play a crucial role in managing various risks. The study reveals how mid-level managers effectively maintain and expand their professional roles. Many news managers take on additional responsibilities by providing mentorship and establishing support systems for their staff, helping them navigate both their professional and personal lives.
Deep learning for impervious surface segmentation from high resolution aerial imagery
Authors:   , Batmunkh Anar
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] The rapid urbanization of landscapes presents significant environmental and ecological challenges, which necessitates the precise mapping of impervious surfaces. This study aims to leverage the capabilities of deep learning, specifically U-Net architectures, for the semantic segmentation of impervious surfaces from high-resolution aerial imagery. Our approach enhances the conventional U-Net architecture to improve its efficiency and accuracy in handling the spatial complexity of urban landscapes. We utilized a dataset comprising aerial images from urban and suburban areas within the city limits of Columbia, Missouri. The datasets are annotated for training, validation, and testing. The models evaluated include U-Net, Residual U-Net, Attention U-Net, Attention Residual U-Net, scSE U-Net, and scSE Residual U-Net. The models were trained and validated under different conditions, with performance metrics calculated for the overall testing datasets. Our results from the 2017 (leaf-on conditions) and 2019 (leaf-off conditions) test sets indicate improvements. The scSE Residual U-Net model achieved the highest IoU of 88.0 percent, precision of 93.6 percent, recall of 93.6 percent, F1 score of 93.6 percent, and pixel accuracy of 94.6 percent. These results underscore the effectiveness of advanced U-Net models in delineating impervious surfaces with high precision and highlight their potential in remote sensing applications for urban planning and environmental monitoring.
Influence role recognition and scholar recommendation in Academic Social Networks
Authors:   , Edara Lakshmi Srinivas
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] Identifying scholars and their relevant publications within interdisciplinary collaborations in an Academic Social Network (ASN) is pivotal for advancing scientific knowledge. This task is inherently complex and time-consuming, requiring precise recognition of a scholar's influence role within a team for specific research activities. This thesis introduces the novel "ScholarInfluencer" recommendation system, which employs a two-pronged approach: (a) a classification model combined with network analysis on a heterogeneous knowledge graph identifies scholar influencers within interdisciplinary teams, and (b) a large language model (LLM) utilizes these influence role recognition results to respond to user queries, generating pertinent scholar and publication recommendations. This innovative approach involves constructing a heterogeneous knowledge graph from ASN datasets, which include entities such as scholars, publications, research grants, and their interrelations. The efficacy of the ScholarInfluencer system is evaluated using four prominent ASN datasets: NSF, DBLP, Cora, and CA-HepTh. The results indicate that the influence role recognition model significantly surpasses existing models, particularly showing a 13.6 percent improvement on the NSF dataset. Additionally, the recommendation model incorporating role recognition consistently outperforms its counterpart without role recognition across all datasets, with a notable 7 percent higher performance on the NSF dataset.
A study of building intelligent systems using reinforcement learning for remote instrumentation and immersive learning
Authors:   , Kambhampati Anirudh
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] Reinforcement Learning (RL) has the potential for developing intelligent, adaptive systems and foster feedback-oriented training of software to make decisions that maximize rewards, and increase accuracy of outcomes. However, challenges such as the complexity of real-time analysis in dynamic environments, ensuring data quality and diversity for robust training, and making RL models interpretable for stakeholders present unique research challenges. In this thesis, we explore the design and implementation of RL-based algorithms to develop intelligent systems within two distinct domains: (i) remote instrumentation for intelligent image analytics in materials manufacturing, and (ii) virtual reality for intelligent pedagogical assistance in immersive cybersecurity education. In the first domain, we present the design of an intelligent system for real-time image processing tasks in a cloud-based platform designed to automate and optimize the process of image analytics in scientific experiments. Specifically, we develop a Remote Instrumentation Science Environment (RISE) platform that integrates remote instruments and data centers using cloudlets, providing seamless access through a high-level language API. By employing RL agents, RISE enables real-time analysis and dynamic adjustment of instrument settings enhancing experimental control. In the second domain, we present the design of an intelligent Pedagogical Agent (PA) to support adaptive learning experiences in a Virtual Reality Learning Environment (VRLE), "CyEscape". Integrating Deep Q-Network with OpenAI and Unity, the PA is trained on guiding the learners through a dynamic learning environment, featuring escape rooms that create a practical and immersive approach to cyber security education. Through validation experiments of RISE and CyEscape, we highlight the transformative potential of RL in creating intelligent systems that are adaptive, and user-centric to improve pace of discovery in image analytics, and educational outcomes in cybersecurity training.
An evaluation of intramammary infection status in dairy heifers
Authors:   , Novo Alyssa
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] Contrary to expectation, IMI are prevalent in prepartum heifers. Though awareness of the problem has increased, there are still many questions surrounding the etiology and epidemiology of heifer IMI. For example, there is a paucity of studies evaluating IMI status in heifers prior to late gestation. Therefore, it is not known when heifers become susceptible to IMI or when the greatest risk of IMI occurs during the heifer life cycle. Furthermore, persistence of IMI identified during early lactation has been recently evaluated; however, data on the persistence of species first isolated from prepartum IMI, into early lactation, is lacking. Additionally, it is difficult to compare results between studies since there is no gold standard sampling method in primigravid and nulligravid heifers, no universally accepted IMI definition in heifers, and the methods that have been utilized for bacterial speciation vary in their reliability. The first study of this thesis, Chapter 2, addresses the knowledge gaps surrounding IMI prevalence in primigravid and nulligravid dairy heifers through the evaluation of 4 sampling techniques, including a teat canal swab, mammary secretions collected through the teat end, mammary secretion samples collected by cisternal puncture, and fine needle aspiration of the mammary tissue. Our second study (Chapter 3) aims to access the occurrence of IMI within 8 d postpartum and its relationship to the prepartum prevalence found in the first study. Results of these studies suggest that heifers are potentially susceptible to IMI by 6mo of age and, therefore, investigations are warranted to determine the prevalence and incidence of IMI in primigravid and nulligravid heifers. Furthermore, the IMI occurrence in heifers remained relatively constant between midgestation and 8d post-calving, whereas the IMI occurrence increased between initial sampling and 8d postpartum for the animals initially sampled as mature nulligravid heifers (ages 11-16mo). Therefore, while the focus of many treatment and prevention investigations have been primarily focused on late gestation, earlier treatments may be more effective in reducing the IMI prevalence in early lactation. Overall, diagnostic methods utilizing mammary sampling techniques that bypass the teat canal, such as a cisternal puncture or FNA technique, for prepartum IMI diagnosis may aid in developing a more comprehensive analysis of the etiology and epidemiology of IMI in nulligravid and periparturient heifers.
Use wear as an archaeological landscape
Authors:   , Rector Stephen Kerry
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] In 2014 in an unprecedented collaboration between the Capitoline Museum in Rome, Italy, and the University of Missouri Museum of Art and Archaeology known as the Hidden Treasures of Rome project, 249 Roman black gloss pottery objects were provided for chemical and morphological study over the course of two years. During the Hidden Treasures of Rome: Capturing the Life Cycle of Roman Pottery project, Reflectance Transformation Images (RTI) were taken to enhance details on the objects not visible to the human eye, and undertook high-precision, high-resolution 3D scans of sixty-three objects, mostly plates and bowls, for more detailed analysis. This thesis asked the question whether treating use wear on black gloss Roman pottery as an archaeological landscape can aid in the identification and analysis of use wear. It also suggested that novel methods based on the use of blend modes and multiscale image fusion are superior to the use of profile and tangential curvature, and openness. A methodology using heterogeneous data and based on open source software was developed to reorient the 3D meshes, resect areas of use wear, convert them to a format suitable for import in QGIS, and to generate derived data based on the Relief Visualization Toolbox. It was demonstrated that treating use wear as an archaeological landscape and applying techniques of geomorphometrics and terrain analysis based on blend modes and image fusion using the Relief Visualization Toolbox enhanced and emphasized interior use wear on black gloss Roman pottery. Moreover, the method is general in nature and not restricted to vessel interiors nor to Roman pottery specifically. The method also demonstrated it is superior to previous methods based on profile and tangential curvature, and openness.
Understanding landowner perceptions of conservation easements in Missouri's priority landscapes
Authors:   , Thompson McKenna
Published: November 2024

[EMBARGOED UNTIL 08/01/2025] Land conservation, particularly through conservation easements, is critical in safeguarding ecosystems, habitats, and recreational spaces on privately owned lands. As the United States aims to conserve 30 percent of its land by 2030, private landowners become pivotal in achieving this goal. This is particularly true in Missouri, where 93 percent of land is privately owned. This thesis research examines the perceptions of selected Missouri landowners who own land in areas identified by the Missouri Department of Conservation to have high conservation value but who, to date, have yet to place a conservation easement on their property. Using interviews, we gathered information from landowners about their land use practices, knowledge of conservation easements, and perspectives on conservation planning. We found various motivators and constraints perceived by landowners. Findings from the study emerged in four themes regarding prior knowledge, land protection, regulations, and finance. Many landowners expressed a lack of understanding and knowledge about conservation easements. Land protection, regulations, and finance were both motivators and constraints. These themes represent both motivations and constraints to adopting conservation easements. These findings provide valuable insights for policymakers, conservation organizations, and landowners seeking to promote and enhance conservation easement adoption.