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Communication Protocol Design and Optimization for Underwater Wireless Sensor and Autonomous Vehicle Networks
No full textElectronic Thesis or DissertationUnderwater wireless sensor and autonomous vehicle networks play an increasingly important role in deep-sea exploration, ocean monitoring, and multi-robot coordination. However, they face critical challenges due to low bandwidth, uncertain mobility, interference, and limited energy. This dissertation presents a three-part research effort to improve scalability, energy efficiency, and robustness in underwater AUV communication. The first work addresses routing under positional uncertainty by introducing UL-VBF, a lifetime- and uncertainty-aware extension of Vector-Based Forwarding. UL-VBF models node positions as spatial intervals and uses interval arithmetic and optimization tools to dynamically decide when the node can forward the messages. The computation considers residual energy and location drift to improve forwarding resilience. The second work builds on this by focusing on global network lifetime optimization. It introduces an offline framework using Genetic Algorithms to compute per-node waiting times that account for sampled mobility uncertainty, enabling balanced energy consumption without incurring runtime overhead. Finally, the third work turns to the problem of coordinated scheduling in AUV networks. It proposes a deadline-aware traffic scheduling mechanism that integrates message priority and interference mitigation. Through multi-level queues, cluster organization, and slot-based scheduling, the mechanism enables collision-free, priority-aware communication across multi-task areas. Together, these contributions address key challenges in routing, optimization, and scheduling across different architectural layers of AUV and sensor networks. Evaluations of each work are described in later chapters
Community Health Workers in the Rural South: an Evaluation of the Experiences and Work of Alabama's CHWs During and After the COVID-19 Pandemic
No full textElectronic Thesis or DissertationThe COVID-19 pandemic profoundly underscored the indispensable role of Community Health Workers (CHWs) in mitigating health disparities, especially within structurally vulnerable communities. Functioning as vital liaisons between these communities and the healthcare system, CHWs are instrumental in improving health outcomes, enhancing access to care, and reducing healthcare costs. The central research question of this project explored how the pandemic affected CHW work in Alabama and how that work has subsequently changed through a sequential mixed-methods approach. Findings indicate that the core work of Alabama's CHWs did not fundamentally change; the scope of services provided by CHW expanded to address pandemic-related challenges. The pandemic appears to have been a pivotal moment for the development of the CHW workforce in Alabama as public visibility, state-wide organization, and local government support have continued to grow since 2020. Further investigation is needed to determine the lasting impact of the pandemic and changing funding models on Alabama's CHWs
Implementation of a Healthcare Provider Informatics Team
No full textDNP ProjectThe integration of a healthcare provider informatics team is critical to advancing patient care, enhancing clinical decision-making, and improving the operational workflows of clinicians. With the implementation of the HITECH Act, clinicians faced new challenges in navigating healthcare technologies designed to improve patient care. This project established a dedicated healthcare provider informatics team within Ballad Health to support data-driven clinical decisions and optimize healthcare delivery. Given the increased reliance on technology and the electronic health record (EHR), the role of an informatics team proved essential in bridging the gap between clinical care and technology. The proposed project followed a structured implementation plan, utilizing current and prospective healthcare provider informaticists in forming the informatics team. This team identified gaps in critical workflows, assessed needs for innovative technologies, and pinpointed areas for improvement. A combination of quantitative and qualitative methods was used to evaluate the impact of the informatics team on clinical workflows and staff satisfaction. Key performance indicators included the use of Epic Signal to measure efficiency, Epic Gold Stars to assess configuration and adoption, and provider surveys to assess needs and satisfaction. Quantitative and qualitative data was collected and analyzed before and after - implementation of the healthcare provider informatics team. The findings demonstrated measurable improvements in system usability and overall provider satisfaction.
This project highlighted the critical role that a healthcare provider informatics team played in bridge the gap between technology and clinical practice. By addressing the challenges healthcare providers faced with EHR systems, the team contributed significantly to improving clinical workflows and enhancing staff satisfaction with the EHR
World heritage and inter/national cultural prestige
No full textOpen Access ArticleWorld heritage has become UNESCO’s flagship programme, and it is a site of active state engagement. At the crux of that engagement is the prestigious World Heritage List. This engagement is regularly analysed as pursuits of national prestige. In this article, I advance a Bourdieusian analysis of world heritage as a field that generates international cultural prestige. I identify humanity as the field’s doxa that allows for a vertical separation and the generation of more-than-national cultural value. I show how states’ desire for this prestige jeopardised the field’s autonomy at a critical juncture in 2010 and analyse the field’s aftermath as involving fraught attempts by states to discursively reconstruct the field’s vertical and functional separations in the quest for international cultural prestige. This reconstruction involves nothing less than reinterpreting humanity as the community-of-states, pointing at once to humanity’s indispensability for more-than-national value and undermining its ability to generate that value
Intelligent Treadmill Control and Holographic Rendering for Accessible Rehabilitation
No full textElectronic Thesis or DissertationThere is a growing need in the medical rehabilitation market due to the increasing elderly population. More than half of the patients are outpatients who require commuting from home to nursing facilities. However, the geographic distribution of these facilities is highly imbalanced, with states like Texas and California housing the largest number, while many rural and remote areas face a shortage. This disparity creates a significant burden for patients who require continuous rehabilitation but struggle with long commutes. Addressing this gap in rehabilitation accessibility is the central focus of this dissertation. This work approaches the problem from two complementary directions: (1) reducing the cost of home-based rehabilitation equipment, and (2) advancing telerehabilitation technologies. The first part of the dissertation introduces an intelligent treadmill control system that enables a single-belt treadmill to function like a split-belt treadmill, thereby providing a cost-effective solution for post-stroke gait rehabilitation at home. This system integrates real-time gait classification models and adaptive speed control to simulate split-belt dynamics without requiring specialized hardware. The second part explores novel telerehabilitation solutions. We adopted advanced neural rendering techniques to build a low-cost 3D patient reconstruction pipeline to enhance remote patient monitoring and engagement. Through these innovations, this dissertation contributes to making rehabilitation more accessible, affordable, and effective, particularly for patients in underserved areas. The proposed solutions aim to bridge the gap between clinical rehabilitation and home-based care, ultimately improving patient outcomes and reducing healthcare disparities
“Teach Or Punish?”: a Qualitative Case Study of a Restorative Approach to School Discipline to Focus on the Moral Development of Students
No full textElectronic Thesis or DissertationExclusionary disciplinary practices have been utilized by American schools for decades and have ultimately contributed to the school-to-prison pipeline. Reports from the U.S. Department of Education and the Office of Civil Rights indicate the disproportionate rates that students of Color, particularly those who are Black boys, are excluded from instruction due to behaviors at much higher rates than their white peers. This data also reflects how students of Color who receive special education services are also disciplined at excessive amounts when compared to white, non-disabled students. To combat this current problem in education, many education reformers and leaders have opted to implement more restorative approaches to discipline. For this qualitative case study, I examine how one specific school is combatting the school-to-prison pipeline through their efforts to promote restorative methods for school discipline. The school, Rise Academy, recognizes the current problem in education regarding school discipline and instead focuses on the overall growth of their students to nourish them and help them thrive by prioritizing trauma-informed care, social emotional learning, response to intervention, and restorative practices, amounting to a form of moral education Keywords: school discipline, restorative justice, moral development, virtue, characte
Arabaribiti, for SATB Voices Divisi
No full textElectronic Thesis or DissertationÀràbàríbítí (The Mighty) is an African art choral composition for SATB voices, divisi, with a performance duration of approximately eleven minutes. The piece is scored for a large SATB choir of 40 or more singers in three movements (with multi-layered subsections), two of which are a cappella and one accompanied movement featuring marimba, woodblock, and shakers (shekere). The piece interplays between three flat major key signatures—B-flat, E-flat, and G-flat—and also between various meters: 4/4, 6/4, 3/4, and 2/4. The composition explores and preserves the linguistic contours of the Yorùbá language, which is rich in tonal inflection, while also engaging Western musical traditions and compositional techniques. Hence, the texts are carefully set to music with consideration to the melodic contour that mirrors the meaning of the texts in the Yorùbá language, with the melody largely adhering to the natural and tonemic speech patterns of the Yorùbá language. The harmonic texture of Àràbàríbítí is generally tonal. However, to avoid monotonous harmonic results, enhance the linguistic contour, and give room for musical creativity, modal mixture and chromaticism were employed. Although the work alternates between major key tonalities, it also engages with frequent modulations and several dissonant harmonic shifts. Rhythmically, the composition employs both simple and complex polyrhythmic layering while also engaging augmentation, diminution, and text painting to accentuate the imagery of nature and the divine. A celebratory middle movement, marked by a dance-like 6/4 meter, introduces the percussive accompaniment, depicting the communal and celebratory Yorùbá musical traditions.Dynamic shaping, expressive markings such as swell, crescendo, decrescendo, and articulation techniques like marcato and staccato are precisely indicated throughout the score. Melismatic passages and metric freedom occasionally suspend the original pulse to enhance flexibility and fluid musical gestures. This music represents a creative fusion of traditional Yorùbá musical idioms with Western choral techniques. The work demonstrates the composer’s engagement with African art music, contributing to the cross-cultural dialogue between African and Western musical practices while advocating for unity and harmonious coexistence between humanity and nature
Bound by Books and Bonds: a Narrative Inquiry into the Lifelong Learning of Black Women Participating in a Book Club
No full textElectronic Thesis or DissertationIn this narrative inquiry, I explore the lifelong learning experiences of Black women participating in The Crowned Sisterhood book club's related activities. The underrepresentation of Black women in educational psychology and the omission of non-formal learning sites in adult education literature guide the focus of my research study. Drawing from lifelong learning, the situative approach, and intersectionality for the conceptual framework, the (re)presented narratives provide insight into the learning trajectories of Black women across diverse settings and within personalized contexts
Financial Well-Being of Military Veteran Retirees Compared to That of Non-Veteran Retirees in the United States
No full textElectronic Thesis or DissertationFinancial well-being (FWB) is a critical factor in assessing the financial status of an individual’s financial stability and quality of life. Little attention has been paid to the use of FWB in the examination of veterans in retirement. This study explores FWB among veterans in retirement using data collected in the 2018 wave of the FINRA Investor Education Foundation’s National Financial Capability Study (NFCS). Three subgroup samples were identified as veteran retirees (have served less than 20 years in the Armed Forces), retired military veterans (served in the Armed Forces for at least 20 years), and non-veteran retirees. A multiple regression was conducted on a sample of 6,116 relevant respondents, which was leveraged to examine the levels of FWB veterans are experiencing in retirement compared to their non-veteran counterparts. The regression analysis shows that financial satisfaction, objective financial knowledge, and perceived financial capability is positively associated with higher FWB (p<0.001). Veteran retirees experience significantly higher FWB than non-veteran retirees. Retired military veterans experience higher, but not significantly higher, FWB outcomes than non-veteran retirees. This study could have positive implications for current members in the United States (US) military regarding recruitment and retention when members consider FWB in retirement. Future research should explore the impact that certain financial literacy curriculum has on the veteran retirement population
Model-Based Knowledge-Driven Machine Learning for Automotive Radar Imaging and Perception
No full textElectronic Thesis or DissertationThis thesis enhances automotive radar object detection by integrating deep learningnetworks with radar signal processing expertise. Automotive radar sensors are essential inadvanced driver assistance systems and autonomous vehicles due to their low cost,robustness, and effective operation in all weather conditions. Cameras and LiDAR systems,while offering advanced environmental perception, suffer performance degradation inadverse weather and poor visibility and often have higher costs. Millimeter-waveautomotive radars, operating between 76–81 GHz with bandwidths up to 4 GHz, providehigh range resolution and strong penetration capabilities through fog, rain, snow, smoke,and dust. Despite these advantages, radar’s potential for object detection and classificationremains underutilized due to limitations in angular resolution, reliance on sparse pointclouds in commercial systems, and the scarcity of publicly available high-resolutionautomotive radar datasets.To address these challenges, this thesis focuses on three key enhancements. First, wepropose novel deep learning frameworks for Direction of Arrival (DOA) estimation, aimedat improving angular resolution and object localization accuracy while simultaneouslyreducing system complexity. Second, by integrating deep learning into the radar signalprocessing pipeline, we enhance feature extraction from raw radar data. This integrationnot only improves radar image quality but also increases the reliability of subsequentobject detection and classification tasks. Third, we develop the BAMA Radar Dataset, acomprehensive collection of radar data with corresponding LiDAR and camera data,specifically tailored for autonomous driving scenarios and diverse environmental conditions.This dataset fills a critical gap, as existing autonomous vehicle perception datasets oftenprioritize camera and LiDAR recordings, with limited radar data. Using this dataset, wedesign and implement an object detection network optimized for high-resolution radarimagery, addressing the unique characteristics of radar data to enhance detectionperformance. The network is trained and evaluated on our dataset and other public radardatasets, ensuring robust validation of its capabilities.Through these advancements, this thesis enhances the capability of automotive radarsystems for object detection and classification in autonomous vehicles. Integrating deeplearning with radar signal processing boosts radar performance and complements existingperception systems, contributing to safer and more reliable autonomous drivingtechnologies