LOUIS University of Alabama in Huntsville
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Implementing telehealth-facilitated cognitive behavioral therapy for insomnia in mental health patients
No full textInsomnia is one of the most prevalent sleep and mental health disorders, often presenting with difficulty falling or staying asleep and resulting in daytime fatigue and impaired functioning. Cognitive Behavioral Therapy for Insomnia (CBT-I) is a first-line, evidence-based, non-pharmacologic intervention; however, access is often limited due to a shortage of trained clinicians, transportation barriers, and systemic inequities. This project implemented a telehealth-facilitated CBT-I program to expand treatment options and improve sleep quality among individuals diagnosed with insomnia. The initiative introduced an evidence-based intervention into a Federally Qualified Health Center (FQHC) that previously had not offered this service. The population included adults aged 18–65 diagnosed with insomnia per the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR). Participants were referred by primary care providers, psychiatrists, psychologists, and psychiatric-mental health nurse practitioners. Six participants completed an eight-session CBT-I program over four to eight weeks. Each session incorporated core components of CBT-I, including sleep education, sleep hygiene, sleep restriction, stimulus control, cognitive restructuring, and relapse prevention. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI) pre- and post-intervention. Median PSQI score improved by almost 69 %, from 15 to 4.67 (p = 0.031), making a clear shift from poor to good sleep quality. Across six participants, a total of 48 telehealth sessions were delivered by a single psychiatric–mental health nurse practitioner, demonstrating that the new CBT-I service line is both feasible and scalable within an FQHC setting. Participants reflected the racial and ethnic diversity of Oregon’s population, indicating that telehealth can facilitate CBT-I and promote equitable access for underserved adults. Collectively, these findings highlight feasibility, intense engagement, and meaningful clinical improvement within a federally qualified health center
Design and synthesis of crosslinkable amphiphilic PEGylated poly(amino acid) copolymers for SPION encapsulation
Get PDFThe development of polymeric micelles for biomedical applications has been of significant interest due to their potential for targeted delivery for therapeutic and diagnostic agents. This dissertation focusses on the synthesis, characterization, and application of amphiphilic PEGylated poly(amino acid) block copolymers as nanocarriers for superparamagnetic iron oxide nanoparticles (SPIONs) in magnetic particle imaging (MPI). Series of novel diblock and triblock copolymers were synthesized through ring-opening polymerization of amino acid N-Carboxyanhydrides (NCAs) to afford HO-PEG77-b-p(L-Leu)m and HO-PEGx-b-p(Allyl-L-Gly)10-b-p(L-Leu)n. These polymers were further functionalized to yield their folate conjugates through dicyclohexylcarbodiimide (DCC) coupling. NMR and FTIR were used to verify the chemical structures of the block copolymeers. The copolymers were further characterized by TGA and DSC and it was found that increasing the hydrophobic block led to copolymers that were thermally more stable. The resulting amphiphilic block copolymers were self-assembled into micelles. The encapsulation of SPIONs within the micelle core was explored, thus confirming their potential for magnetic particle imaging (MPI). Photoinitiated crosslinking was employed to enhance micelle stability. It was shown that these crosslinked micelles were able to retain structural integrity after lyophilization and exhibited no measurable critical micelle concentration (CMC), which signifies successful crosslinking. TEM measurements showed the micelles morphology and size. The size data were corroborated by DLS studies, which also indicated a narrow size distribution
How sweet it is : implementation of a web-based application to assist with adherence to diabetes treatment and improve glycemic control
No full textThis was a new program implementation that used evidence-based guidelines to implement the use of mHealth technology via text messages to improve patient adherence to treatment plans and ultimately improve glycemic control. Patients were selected from within an established Internal Medicine practice based on a diagnosis of diabetes and HbA1c greater than 7.5%. Participation in the study was voluntary. Using the self-care of diabetes inventory, self-care was also measured. Baseline HbA1c, BMI, and SCODI scores were collected. Patient education was provided following the American Diabetes Association’s recommendation for patient education. The patients downloaded and utilized the application one drop, and received a daily text message related to their diabetes treatment. Messages were received for a 12-week period, at which time the variables were assessed and analyzed. SPSS software was used for the descriptive analysis of the data. The data reflected statistically significant improvement in glycemic control and self-care abilities. The stakeholders were presented with the findings and a plan to implement the intervention as a standard of care within the practice, as well as a plan on how to integrate this into their practice
Subconscious warnings as a safety mechanism against phishing attacks
Get PDFPhishing attacks are very common and considerably successful in stealing people’s information, but unfortunately normal security procedures fail to warn the user of their possible dangers. By considering how subliminal messages are very effective in informing people without actively disrupting their attention, a prototype of a browser extension was made by implementing subconscious messages and the elements of an effective warning, in an attempt to subconsciously warn users of possible phishing attempts, with minimal impact on browser performance
Heat transfer of additively manufactured turbine alloy blade tips with different surface enhancement post-processing methods
Get PDFThe investigation results presented here consider the heat transfer characteristics of seven different turbine blade tips. Six of the blades are additive manufactured with GRX-810, followed by different surface enhancement post processing techniques. Those techniques include as built, abrasive flow machining, chemical polishing with chemical mechanical polishing, micromachining, chemical polishing, and electropolishing. The seventh blade is machined with 1016 steel. These procedures give a different surface texture roughness for each blade. A transonic linear cascade within a transonic/supersonic wind tunnel is utilized. The impulse response method is employed to determine heat transfer coefficient distributions for the blades. The Tad/T0i temperature ratio distributions are used to determine the tip gap Mach number distributions for the blades. Local heat transfer coefficient values along the blade tips are generally larger with the rougher surface texture relative to the smoother surface textures, which are associated with locally lower tip gap flow Mach numbers
Deep learning approaches for semantic segmentation of hyperspectral satellite imagery
No full textRemote sensing plays a crucial role in Earth and environmental sciences, providing valuable data for monitoring and understanding our planet. This technology allows for the collection of spatial information from a distance, enabling researchers to study large areas efficiently. Hyperspectral imaging is an advanced form of remote sensing that collects data across hundreds of narrow, contiguous spectral bands. Hyperspectral image classification is crucial in remote sensing as it allows for accurate segmentation of land cover using sophisticated machine learning methods. This research investigates the performance of two deep learning architectures, namely UNet and the Multi-Layer Perceptron (MLP), in the context of semantic segmentation for hyperspectral images captured by DESIS. The UNet architecture, built upon a ResNet34 backbone, effectively captures spatial characteristics utilizing an encoder- decoder framework. Meanwhile, the MLP focuses on pixel-specific spectral data, extracting distinct features across 235 hyperspectral bands. An extensive collection of stratified hyperspectral samples was used for both training and evaluation, resulting in high classification accuracy for various land cover categories. Comprehensive quantitative and qualitative evaluations, incorporating classification reports, confusion matrices, and segmentation visualizations, reveal both the advantages and drawbacks of each method. The results indicate that deep learning models, namely UNet is capable of hyperspectral image classification with 92 % accuracy in capturing spatial features with nominal overfitting. Our baseline model, a simple MLP did not produce acceptable accuracy across all the images, working well for some while doing the opposite for others probably due to the inaccuracies present in the ground truth mask used for training. Our framework and the associated results have the potential to contribute towards efficient processing of hyperspectral images
Simplified soil-based approach for maximum pressure prediction in horizontal directional drilling operations
Get PDFHorizontal Directional Drilling (HDD) is a trenchless pipeline installation method used in areas where traditional trenching is not feasible due to environmental concerns, existing infrastructure, or other constraints. However, HDD presents significant engineering challenges, with design failures often resulting from excessive installation stresses and Hydro Fracture. Hydro Fracture occurs when the pressure of drilling fluid exceeds the soil’s bearing capacity, leading to unintended subsurface fractures. To mitigate this risk, researchers have relied on the Delft Equation to estimate the maximum allowable mud pressure for borehole stability. However, this equation does have notable limitations and assumptions, which may not accurately reflect real-world conditions. This research will analyze real field data from HDD projects and compare observed results with a simplified equation. The study aims to identify discrepancies, refine predictive models, and propose improvements to enhance borehole stability assessments and reduce the risk of Hydro Fracture in future HDD applications
An Investigation into the Effects of Explosive Based Shockwaves and Heat Generation Imparted on Bomb Squad Helmets
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