University of Tennessee Institute of Agriculture
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Understanding the Associations Among Trauma Symptoms, Impulse Control, and Spirituality
No full textAdverse childhood experiences (ACEs) are common and have been linked to a variety of negative outcomes, such as trauma-related symptoms and, in some cases, difficulties with impulse control. However, less is known about how spirituality might influence these relationships in youth. The current study examines the associations among ACEs, trauma-related symptoms, impulse control, and spirituality in a sample of 252 children aged 10-17 years of age. Using archival data from the National Institute of Justice, we examined whether (1) the effect of ACEs on trauma-related symptoms is moderated by spirituality and (2) the effect of ACEs on impulse control is moderated by spirituality. We propose that spirituality will (1) weaken the positive relation between ACEs and trauma-related symptoms and (2) weaken the positive relation between ACEs and impulse control problems. In examining the interaction among these concepts to better understand the impact of childhood trauma and the role of spirituality as a potential protective factor, we found that spirituality strengthened the relationship between ACEs and trauma-related symptoms, while no association was observed between ACEs and impulse control problems at any level of spirituality. Research and clinical implications are discussed
SHIFTY BEHAVIOR: USING PUPILLOMETRY TO DETECT COVERT SHIFTS OF ATTENTION IN INFANTS
No full textEnvironments have an overwhelming amount of visual input, therefore requiring a system that works diligently to select and filter the most relevant, or salient, information. Posner and colleagues posited a spatial “attentional spotlight” that is shifted by the viewer to new locations either accompanied by an eye movement, overtly, or while eyes are fixated elsewhere, covertly. These covert attentional shifts typically precede an eye movement facilitating efficient visual scanning. This critical component of pre-verbal learning develops around 4-months in infants. Adult research pairs behavioral responses with verbal or button box responses to determine the speed and capabilities of covert orienting. The purpose of this study was to pair traditional reaction time and accuracy measures with pupillometry to further inspect developmental and conditional differences of covert orienting across infancy. A linear mixed effect (LME) model revealed that pupil dynamics were reflective of developmental differences, with older infants showing significantly greater dilation than younger infants across multiple conditions. Further, conditions that required inhibition and re-orienting showed significantly greater dilations when compared to both the baseline (no visual cue condition) and the valid cue conditions. Taken together, results from this study suggest pupillometry may bea sensitive marker of attentional orienting, though dilation may be more reflective of saccade inhibition and attention re-orienting
Estrous Active Behaviors and Other Factors Related to Higher Estrous Associated Temperatures in Beef Cattle
No full textEstrual cattle exhibit varying levels of estrous active behaviors and higher estrous-associated temperatures (HEAT), both influential in preovulatory follicle progression and contents important for fertility outcomes. Previous analyses utilizing descriptive animal variables left 52 to 95% of HEAT variation unexplained. Increased walking alone elevates body temperature, leading to the hypothesis that estrous active behaviors (e.g. mounting or standing to be mounted) significantly contribute to HEAT variability. The objective of these studies were to: 1) characterize estrous active behaviors in beef cows and heifers, 2) determine to what extent estrous active behaviors are associated with HEAT, 3) determine the ability of estrous active behaviors to predict vaginal temperature (VTp) during HEAT using machine learning models, and 4) determine which variables were more predictive of HEAT than others. Estrus was induced using gonadotropin-releasing hormone, a seven-day progesterone-infused controlled internal drug release (CIDR) application, and prostaglandin F2α (PGF2α) administration. HEAT onset was defined when VTp (monitored via iButton attached to a blank CIDR) increased 0.1°C above baseline. Estrous behaviors, including mounting and standing to be mounted, among others, were recorded and summarized every 15 minutes alongside the number of sexually active animals (SAGsum). Additional activity measures were captured using collar-based triaxial accelerometers, and ambient temperature-humidity index (THI) was recorded hourly. Estrus was observed in 93.3% of cows (14/15) and 83.3% of heifers (20/24). Average HEAT duration was 21.3 hours (cows) and 16.3 hours (heifers). Hierarchical linear regression indicated behaviors such as mounting others and standing to be mounted significantly associated with VTp increases (0.03 to 0.05°C per event), explaining up to 54% of HEAT variation. An ensemble machine learning model incorporating 66 predictors (behavioral, accelerometer-derived, ambient, and descriptive animal variables) achieved improved predictive accuracy. Using Shapley value analysis, the dataset was reduced to 12 highly predictive variables, enhancing computational efficiency and accuracy, explaining 66.3% of HEAT variability. Animal weight and SAGsum emerged as most predictive, followed by accelerometer-derived metrics and observed estrous behaviors. This research underscores the predictive value of estrous behaviors on HEAT, offering potential for optimized fertility management through integrated machine learning approaches on-farm
Spiking Ocular Controlled Robot
No full textEmbedded neuromorphic applications propose the unique blend of analog computational practices within the more classically digital embedded framework. The neural network itself has made headway into numerous fields including data analytics and self-driving vehicles. It offers a robust processing capability for multi-variable and multi-state implementations. Neural networks present a novel approach to solving problems of scaling size. Each input sensor receives its own weights, which each layer of the network may interact with, ultimately coalescing in a single output collectively decided by the network. In conjunction, embedded applications have provided numerous solutions for Internet of Things (IoT) applications. These include small-scale circuits regulated temperature to more advanced traffic-signal modules. Usually, these boil down to what is known as a ”control” application. Some input amends the current state of the system, and the central controller attempts to return to a homeostasis. This work attempts to utilize a neuromorphic framework in place of a more typical digital controller in order to localize image processing and control. The controller will operate a camera and a remotely-controlled robot in response to what it identifies on camera
MONITORING THROUGH DEEP LEARNING: POULTRY ANIMAL FEEDING OPERATION DETECTION AND WATER DEMAND ESTIMATIONS IN TENNESSEE
No full textUnderstanding the scale and distribution of poultry production facilities is essential for managing agricultural water use, environmental risk, and infrastructure planning, particularly in regions with limited regulatory oversight. This thesis presents a novel, remote sensing-based framework to geolocate and quantify poultry barns across four major production counties in Tennessee: Bradley, Bedford, Weakley, and Henry. Using high-resolution USDA NAIP imagery and a DeepLabV3 image segmentation model, over 1,380 poultry barns were detected with high spatial accuracy (87.8% IoU; 92.6% F1/Dice), including 518 in Bradley, 374 in Bedford, 356 in Weakley, and 133 in Henry. A post-processing pipeline removed false positives and filtered detections to match known barn size profiles, enabling the creation of a new spatial database of poultry infrastructure. Clustering analysis using the DBSCAN algorithm revealed 27 distinct barn clusters and 442 individual unclustered detections. High-density clusters, such as Cluster 11 in Bradley County (98 barns) and Cluster 26 in Bedford (76 barns), emerged as key poultry production zones. These clusters were used as the foundation for estimating localized agricultural water use.
Annual water consumption was calculated using experimentally validated data from Tennessee broiler operations and standard parameters from the National Chicken Council. A typical 43,325 ft² broiler barn was estimated to support 240,000 birds annually and use approximately 878,000 gallons of water. Extrapolated across all detected barns, total poultry-related water use exceeded 1 billion gallons annually, comparable to the residential water demand of a city like Cookeville, TN (~36,000 people). Comparison with USGS livestock water withdrawal data and TDEC CAFO permits revealed discrepancies, especially in counties where poultry barns lacked permits or where water use estimates included other livestock types. These findings expose the shortcomings of existing livestock reporting systems and emphasize the importance of detailed, species-specific spatial monitoring. This study delivers a transferable geospatial framework for identifying agricultural operations and forecasting water use. With continued refinement, it offers a powerful tool for environmental oversight, infrastructure planning, and sustainable livestock management across the southeastern U.S
Reach: A Poem
No full textThis project is a book-length, prosimetric poem that brings confessional sensibilities to the formal legacy of projectivist poetics. Inspired by Charles Olson’s “Projective Verse,” Reach: A Poem gathers a chorus of secondary voices through epigraphs and interpolations that illuminate the speaker’s own voice. This allusive material supports the personal, however, as the poem considers the ethics of lyrical and confessional forms of representation throughout its exploration of familial narratives and memory. The task of Reach, therefore, is to claim an inheritance of literary and familial predecessors who co-create a poetics of imaginative empathy, where all subjects receive care and tenderness in their representation
Drivers of bryophyte community change across the Appalachians
No full textWork exploring the drivers of vascular plant community change through time are common throughout the literature. The literature pertaining to their non-vascular relatives, the bryophytes, however, is dwarfs in comparison. Despite bryophytes providing key ecosystem functions from providing soil stability, sequestering carbon, and providing habitat for macro and microorganisms, the drivers behind bryophyte community change have been assumed to be akin to those of vascular plants; despite literature which demonstrates key differences in how bryophytes respond across gradients of abiotic and biotic factors. As such, this dissertation explores these drivers to understand what drives bryophyte communities through time, using the bryophyte communities of the Appalachians as a case study.
I used a longitudinal study, herbarium records, and ecological niche modeling to examine 1) What environmental determinants drive bryophyte community change as well as to identify the current state of bryophyte communities 2) How differing management strategies for an invasive pest have impacted bryophyte communities in the past and 3) How macroclimatic variables may determine suitable habitat for species both now, and in the future. The results from these studies finds that elevation, aspect, slope, and gap fraction all play roles in determining bryophyte abundance and/or richness, that extreme management strategies may have negatively impacted bryophyte richness, that substrate generalists are more likely to persist through time following a disturbance, and that climate change will reduce suitable habitat across all bryophytes, but that there are certain vulnerable species that are more likely to experience extirpation if their suitable habitat is not managed effectively.
Results from my dissertation are useful for ecologists, land managers, and policy makers when considering drivers of bryophyte community change. This will allow for the better preservation of bryophyte communities at both the community and species level, and consequently, better protect the ecosystem functions they provide
Forced Oscillation Analysis and Model Reduction Techniques in Power Systems
No full textThe presence of forced oscillations and poorly damped low-frequency oscillations poses significant challenges to the operation of modern interconnected power systems. These phenomena can lead to severe consequences, including damage to critical equipment, reduced efficiency in power transfer, and compromised overall stability of the grid. Forced oscillations, often triggered by periodic disturbances, propagate through networks, amplifying stress on system components. Similarly, low-frequency oscillations, arising from weak system damping, can persist and undermine operational reliability. Addressing these challenges is critical to maintaining the safety and resilience of power systems amid increasing complexity and demand. The first part of this dissertation investigates how different exciter and governor model parameters influence the magnitude of forced oscillations. Chapter 2 examines these parameter impacts, providing insights for power system planners and operators to optimize settings and enhance stability. Chapter 3 explores the forced oscillation frequency impact under resonance conditions. Chapter 4 categorizes oscillation sources, identifying exciters, governors, or renewable energy plants as potential origins. Mitigation strategies using inverter-based resource (IBR) actuators are explored in Chapter 5. The second part addresses the growing complexity of modern power grids, intensified by the integration of intermittent renewable energy sources. Simulating large-scale systems using traditional model-based approaches has become computationally demanding. To overcome this, a measurement-based model reduction approach utilizing vii long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks is proposed. This technique enables efficient and accurate analysis of power system dynamics, offering a scalable solution for managing the challenges posed by renewable integration. This comprehensive dissertation provides a multi-faceted approach to improving power system stability and resilience, offering practical solutions for managing forced oscillations, leveraging advanced machine learning techniques for system analysis