LOUIS University of Alabama in Huntsville
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The Nucleated and Ultra-Compact Dwarf Galaxies of the Coma Cluster
No full texthttps://louis.uah.edu/rceu-hcr/1526/thumbnail.jp
Quality improvement project for pediatric asthma
No full textPediatric asthma remains a significant public health concern, often complicated by inconsistent management practices that contribute to increased morbidity, emergency department visits, and hospitalizations. These gaps in care disproportionately impact vulnerable and underserved populations. Despite established guidelines for asthma management, inconsistent implementation of asthma control test (ACTs), spirometry, asthma action plans (AAPs), and flu vaccine advocacy remains a critical barrier to optimal pediatric asthma control. This Doctor of Nursing (DNP) project evaluated the impact of implementing a comprehensive asthma care protocol guided by the Chronic Care Model (CCM) at a pediatric clinic, targeting patients aged 5 to 18 years with a diagnosis of asthma. The protocol included incorporating asthma control test (ACTs), spirometry, asthma action plans (AAPs), and influenza vaccines. Staff education sessions were conducted to introduce the new asthma care protocol and reinforce the principles of the Chronic Care Model (CCM). Post-implementation results demonstrated an increase in the use of ACTs, spirometry, and AAPs. Influenza uptake could not be measured due to the seasonal timing of the project implementation
Lost in translation: the impact of intermediate language representations on machine learning applications
No full textMachine learning assisted binary analysis is an area of great interest in cybersecurity research. Training accurate machine learning models requires methods of binary lifting, which can require binaries to be translated through an intermediate language representation. This dissertation postulates that different intermediate language representations change the performance characteristics of these machine learning models. This dissertation takes a published machine learning frameworks as a control, modifies their input methodology to include different intermediate language representation transforms, and performs direct comparisons of model performance to ascertain bias in performance caused by different intermediate languages. This research enables the machine learning engineer, focused on binary analysis tasks, to build models with the knowledge on how characteristics of intermediate languages may be biasing output performance
Factors affecting freshwater fish communities in two North Alabama counties
No full textThis study examined how physical habitat characteristics influence freshwater fish diversity in streams of Madison and Limestone Counties, Alabama. Ten 150-meter stream reaches were sampled using backpack electrofishing and seining, with habitat variables measured at fixed transects. Principal component analysis identified four main habitat gradients representing flow and substrate, channel size, gravel dominance, and canopy–bedrock conditions. Although overall species richness and Shannon diversity were not significantly related to these gradients, species-specific correlations revealed clear ecological preferences. Regression models showed that root wads, herbaceous vegetation, and riprap modestly increased richness, emphasizing the role of structural diversity. These findings highlight the importance of maintaining heterogeneous stream habitats to support fish communities in North Alabama and provide a foundation for future habitat restoration and monitoring efforts
Multipoint observations of energetic particle acceleration associated with corotating interaction regions
No full textStream Interaction Regions (SIRs) play a critical role in energetic particle acceleration and transport in the heliosphere. While shocks and compression regions associated with SIRs can accelerate particles to several MeV energies, most of our current understanding of these events is based on observations near 1 AU and beyond. Observations inside 1 AU, particularly by PSP and SolO, provide a unique opportunity to disentangle the roles of acceleration and transport. This dissertation presents the first detailed measurements of SIR-associated energetic particle events in the inner heliosphere between 0.15 and 0.80 AU using PSP, complemented by SolO and multi-spacecraft observations near 1 AU. The events are relatively weak without shocks, yet still show localized suprathermal and energetic particle enhancements. Spectra reveal hardening below 1 MeV, softening above 1 MeV, and no low-energy turnover, pointing to efficient local acceleration at compression regions. This work utilizes observations from PSP, SolO, STEREO-A, ACE, and Wind. This research was supported by NASA-HELIO-FINESST Grant 80NSSC24K1867
Managing medication adherence with a smartphone application to improve blood pressure : an informatics project
No full textHypertension is a widespread issue often linked to non-adherence. Over 50% of U.S. patients do not follow their BP medication prescriptions. This problem exists across the U.S., including Georgia and at the L&D Medical Center clinic in Powder Springs. The clinic has 796 patients, of which 396 have hypertension. Hypertension diagnoses are increasing annually, as evidenced by the clinic having 267 patients with HTN the previous year. The principal aim of this intervention was to improve adherence and lower blood pressure using the MyTherapy app. The app was downloaded onto participants’ smartphones and used to alert patients when it was time to take blood pressure medications daily and to record blood pressure readings. Demographics, systolic BP, diastolic BP, and adherence scores were collected over 12 weeks. The Hill and Bone Adherence questionnaire was administered at the beginning and end of the 12-week period to evaluate adherence. The results showed improvement in systolic and diastolic blood pressure; however, not all participants reached the target of 130 mmHg systolic and 80 mmHg diastolic. All participants successfully scored below 30 on the Hill and Bone Adherence questionnaire, indicating that the MyTherapy app met its goal of enhancing adherence to blood pressure medications. The results demonstrated a significant clinical improvement in BP measurements, along with notably better compliance scores over 12 weeks. The use of the MyTherapy app proved effective across all populations, regardless of age, culture, educational level, or gender
Declarative re-planning : a trustworthy deep reinforcement learning method enabling zero shot learning in mobile robot path planning
No full textNavigation methods are needed for robots to operate in a physical world. Path planning for navigation can be a difficult problem to solve due to uncertainties in the world and the robot. Navigation also must be completed in real time to ensure the robot does not collide with other entities in the world. Declarative re-planning (DRP) is a method for path planning. It leverages hierarchical temporal abstractions to drive learning, and run time efficiencies. It is multiple orders of magnitude more sample efficient compared to non-temporally abstract methods, and the asymptotic run time efficiency is linear for the full navigation process. The design of reward functions are also investigated. The trade off between dense reward functions being easier to learn but may drive side effects and sparse reward functions that hard to learn but are more robust to side effects. DRP drastically reduces the penalty for using sparse reward functions enabling the agent designer to leverage the advantages of sparse reward functions with out the cost. The hierarchical temporal abstraction also yields a heuristic for selecting an optimal discount factor for sparse reward functions. This work demonstrates how DRP is effective at zero shot transfer learning. DRP is able to train a navigation policy on one robot, and be used on other robots iii while remaining equally effective at reaching its goal. The other robots do not need to have the same properties, actuators, or number of actuators. DRP also enables simulation acceleration by allowing lower fidelity trajectory calculations to be used without effecting the outcome of the navigation trajectory. Finally, how navigation can be made trustworthy via the amount of information communicated and the nature of the information shared was investigated. More information improved responders ability to predict a robots future motion. No correlation was found between confidence and ability to predict the future motion
Increase successful outcomes with technology-driven mergers and acquisitions : prioritize post-merger integration risk mitigation
No full textIn the post-COVID-19 era, accelerated engineering advances including Artificial Intelligence (AI) have increased firm’s reliance on technology-driven mergers and acquisitions (M&A) as an expedient strategy to bolster their technological innovation capabilities, retain competitive advantage and ensure sustainability. However, many M&As fail to achieve their strategic goals because of persistent challenges during post-merger integration (PMI). This dissertation adopts and integrates chapter 2 – chapter 4 to identify, theorize, and proactively mitigate the primary recurring PMI challenges that impede successful technology-driven M&A outcomes. Chapter 2 adopts PRISMA guidelines, conducts a systematic literature review, identifies, and synthesizes four persistent PMI risk categories: (1) cultural and strategic misalignment, (2) technological integration issues, (3) financial constraints, and (4) cybersecurity risks. Chapter 3 examines the relationship between pre-merger due diligence and PMI challenges and identifies three pre-merger due diligence risks that exacerbate the PMI phase: (5) lack of early prioritizations of PMI issues, (6) sub-optimal due diligence speed, and (7) cognitive bias. Chapter 4 operationalizes the seven risk categories as failure modes and develops a validated, PMI Actionable Risk Mitigation Strategy (artifact) by employing a Failure Modes and Effects Analysis (FMEA) inspired Design Science Research methodology to analyze the artifact’s utility using 35 completed technology-driven M&As that sought to bolster their technological capabilities by acquiring firms with AI and advanced technologies. Chapter 4 also introduces the new theoretical construct, Escalated Gibberish, which emphasizes information governance controls by deconstructing how poor information quality, interpretive risks, and cognitive distortions impair the pre-merger executive decision-making process and compound PMI challenges. In addition, Chapter 4 also introduces an FMEA-inspired PMI RPN scoring rubric grounded on synthesis of empirical studies, termed PrizRed, which enables proactive assessment of acquiring firms’ PMI risk tolerance in each of the seven failure modes during pre-merger executive decision-making process. This dissertation advances engineering management concepts within the M&A literature by applying systems thinking to accentuate the need to avoid Escalated Gibberish during the pre-merger executive M&A decision-making process and provides practitioners with an actionable playbook to proactively mitigate PMI risks in technology-driven M&A projects and improve the likelihood of attaining M&A strategic goals
