University of Tennessee Institute of Agriculture
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Prognostics and Health Management of Nuclear Power Plant Systems using Advanced Data-Driven Techniques
No full textEnsuring the reliability, safety, and cost-effectiveness of nuclear power plants requires advanced methods for fault detection, diagnostics, and prognostics that can operate under changing conditions and across multiple system scales. This dissertation develops and evaluates data-driven frameworks that span anomaly detection, component-level fault diagnosis, and system-level prognostics for critical nuclear systems. The first study introduces a unified framework that integrates statistical, model-based, and data-driven methods to detect and investigate anomalies in complex engineered systems, demonstrating robust and interpretable detection on SMART valve systems. Building on this, the second study evaluates one-class learning approaches for fault detection in the circulating water system of a nuclear power plant, showing that embedding-based models such as Deep Centered Embedding (DCE) generalize effectively under distributional shifts. The third study addresses fault detection and diagnostics in Fine Motion Control Rod Drives (FMCRDs), an underexplored subsystem critical for reactor control, and demonstrates the use of neural architectures for identifying and localizing electrical and mechanical faults. Shifting focus to prognostics, the fourth study investigates data-driven prediction of condenser tube fouling using simulation data, finding that recurrent neural networks, particularly Long Short-Term Memory (LSTM) models, provide accurate and noise-resistant estimates of remaining useful life (RUL). Finally, the fifth study extends prognostics to the system level by introducing a novel topology-aware health index and comparing machine learning approaches, with Graph Neural Networks (GNNs) emerging as the most effective for capturing inter-component dependencies and delivering reliable system-level RUL predictions. Collectively, these studies contribute a comprehensive methodology for advancing predictive maintenance in nuclear power plants, from anomaly detection to system-level prognostics, and provide a foundation for enhancing the resilience and sustainability of critical energy infrastructure
Filling the Gap: Integrating HIV into the Counselor Education Curriculum
No full textPeople living with HIV (PLWH) are vulnerable to mental health conditions including depression, anxiety, and substance use disorders, which in turn, often affect their health outcomes. Multiple, overlapping structural and social determinants of health exacerbate the physical and mental health concerns of PLWH, presenting a need for services that are responsive to the unique implications of the illness. Professional counselors have the potential to provide such services but must be adequately prepared to do so. In response to the lack of HIV-related professional development for counselors and the minimal preparation of counselors-in-training to serve clients with HIV, this article uses the 2024 CACREP standards of accreditation to provide a framework for infusing HIV-related coursework into counselor training. By aligning HIV education with CACREP\u27s eight core areas, we address a critical gap in counselor preparation, emphasizing ethical practice, cultural competence, and practical applications at the intersection of HIV and mental health
Improving Maternal Mental Health: Implementation of a Standardized Postpartum Depression and Anxiety Screening Program
No full textBACKGROUND: Perinatal mood and anxiety disorders (PMADs) are one of the most common complications of pregnancy and childbirth. There is inconsistent screening and identification of patients at risk of PMADs, resulting in patients rarely receiving appropriate mental health treatment. Untreated PMADs can lead to decreased mother-infant bonding and increased maternal suicide risk.
LOCAL PROBLEM: The setting for this project was a large, urban academic medical center in East Tennessee. There was no standardized process for PMAD screening and notifying providers after a positive screen. The project’s purpose was to implement a nurse-driven PMAD screening process to effectively screen and notify providers of patients with positive depression and/or anxiety screens before hospital discharge.
METHODS: This project was guided by the Evidence-Based Practice Improvement (EBPI) model. Plan-Do-Study-Act (PDSA) Cycles were used to test and improve practice change. Screening rates, nursing and provider documentation rates, positive depression and anxiety rates, and demographic data were measured over a 26-week implementation period.
INTERVENTIONS: Use of the Edinburgh Postnatal Depression Scale (EPDS) and a corresponding clinical decision-making algorithm were implemented to screen all postpartum patients on the postpartum unit.
RESULTS: There was a 91% screening adherence rate, with an 8% positive depression screening rate and a 19% positive anxiety screening rate. The co-occurrence for both positive depression and anxiety screens was 36%. Providers were notified of positive PMAD screens 66% of the time, and providers documented patient evaluation after a positive screen at a rate of 65%.
CONCLUSIONS: Screening all postpartum patients for PMADs prior to hospital discharge can lead to more timely access to mental health resources and treatment. This can help identify patients at risk for PMADs sooner than at the 2- or 6-week postpartum office visit. Continued collaboration and communication among providers and nursing staff at this facility is recommended to improve screening and provider notification rates
SACSCOC Response - Method of Delivery Notification - Innovative Transdisciplinary Studies BS
No full textSACSCOC Response - Program Closure - Wide Bandgap (WBG) Power Electronics Graduate Certificate
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Perinatal Mood and Anxiety Disorder Screening in the Neonatal Intensive Care Unit: An Evidence-Based Maternal Mental Health Improvement Initiative
No full textBACKGROUND: Mothers of infants admitted to the neonatal intensive care unit (NICU) are at a high risk of experiencing perinatal mood and anxiety disorders (PMAD). Undiagnosed PMAD contributes to negative psychosocial and health outcomes for mother and infant.
LOCAL PROBLEM: A process for screening NICU mothers for PMAD did not exist in this Tennessee NICU. The purpose of this evidence-based practice quality improvement (EBPQI) project was to initiate standardized PMAD screening for mothers with infants in the NICU. The aims of this project were to 1) screen 70% of mothers with an infant NICU length of stay (LOS) of 2 weeks or greater, 2) provide 70% of screened mothers with maternal mental health resources, and 3) refer 60% of mothers with positive PMAD screening for further assessment and management within 3 months of implementation.
METHODS: The Evidence-Based Practice Improvement Model guided the project’s planning and implementation using Plan-Do-Study-Act Cycles to refine and test the process.
INTERVENTIONS: Nurses administered PMAD screenings to NICU mothers at 2 weeks, 4 weeks, and 2 months based on a timed and automated task list. An algorithm determined actions based on the mothers’ PMAD screening scores. The Edinburgh Postnatal Depression Scale (EPDS) was used to screen NICU mothers, producing an anxiety subscore and an overall depression score.
RESULTS: 43% of mothers with infants in the NICU with a LOS of 2 weeks or greater were screened for PMADs, and 14% were offered mental health resources at the time of the initial screening. 71% of screen-positive mothers were referred for further assessment, surpassing the aim of 60%.
CONCLUSIONS: Implementation of standardized PMAD screenings increased mental health screening for mothers with infants in the NICU. Recommendations were proposed to enhance the project\u27s sustainability and improve screening and resource rates
