LOUIS University of Alabama in Huntsville
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Effects of a nurse-driven delirium prevention bundle in a step-down ICU
No full textDelirium, a debilitating mental illness, poses significant challenges to patients, families, and healthcare systems, particularly among individuals aged 65 and older. This Quality Improvement (QI) project aims to introduce a best practice multicomponent delirium prevention bundle in a step-down Intensive Care Unit (ICU) floor to enhance knowledge, identification, and delirium prevention. Employing Orlando\u27s Theory of the Deliberative Nursing Process, along with multiple Plan-Do-Study-Act (PDSA) cycles over 12 weeks, this project conducted staff education sessions to establish a baseline understanding of delirium identification, risk factors, screening tools, and implementation steps for the prevention bundle. The intervention occurred in a 16-bed step-down ICU trauma floor staffed by registered nurses and care partners. Throughout implementation, staff completed a checklist during each shift, encompassing CAMs score screening and documentation upon admission and every shift, ensuring proper room environment setup, maintaining updated whiteboards, implementing and documenting ambulation as per physical therapy recommendations, and safeguarding the sleep-wake cycle. Post-education sessions, a significant improvement in knowledge was observed (t(21) = 11.92, p \u3c .001). Among 14 patients, high compliance with the delirium prevention bundle was noted (97%). There was no development of delirium cases, and the average CAM score of zero upon admission remained unchanged throughout the stay. These findings underscore the efficacy of delirium education sessions and multicomponent prevention bundles in averting delirium incidents in step-down ICU trauma settings. Ongoing reinforcement of staff knowledge and support are essential for successfully implementing these processes
Context-aware machine learning for low-burden brain-computer interfaces
Get PDFWe are interested in the utility that artificially intelligent mobile systems such as drones offer to personnel in fast-paced, high-stakes situations such as disaster relief that demand real-time situational awareness. Ideally, these assistive systems place no additional cognitive or physical burden on their user; rather, they should respond to the user\u27s intent with minimal physical or cognitive impact. Artificial intelligence (AI) and machine learning (ML) are already widely applied in both brain-computer interfaces (BCI) and drone navigation. We propose leveraging the robust computer-vision based AI that exists on modern drones to use objects as waypoints and fly a reconnaissance drone mostly autonomously, with electroencephalography (EEG) in an object recognition paradigm for selecting the drone\u27s waypoint. In this work, our goal is to provide a proof-of-concept for the intent recognition portion of this design through a context fusion approach that allows selecting a waypoint without using existing techniques that require environmental modification or techniques such as Rapid Serial Visual Presentation (RSVP) that do not translate to kinetic situations. We outline a framework we call Human Intent-Guided Autonomous Systems (HI-GAS) as a general paradigm for this type of system-of-systems that facilitate human-AI teaming by using decision fusion between biosignal-based intent recognition and sensor-borne context awareness. We introduce the Context-Signal Decision Fusion (CSDF) model to merge EEG with imagery and conduct a 42-subject experiment to explore its viability, requirements, performance, and dynamics. In the end, we show that CSDF shows potential for implementation in the wider HI-GAS framework even with relatively low-cost, portable hardware. We evaluate the model under a variety of dynamics, identify results regarding subject-independence and architectural variation, and present mechanisms to explore the model from an explainability perspective
The effects of reducing carbon content in the processing of superalloy Inconel 718
Get PDFThe properties of Inconel 718 (IN718) are dependent on the elemental composition and microstructure. Applications using IN718 include rocket and jet engine parts. These parts are often complex and difficult to machine, making IN718 ideal for additive manufacturing processes such as laser-powder bed fusion (L-PBF). The various phases that form in IN718 rely on the niobium (Nb) content including the carbides. The precipitates also include Nb in the strengthening phases of gamma prime and gamma double prime. Overaging of gamma double prime transforms the metastable phase into the stable delta phase, which also controls grain size. This study evaluates the effects of reducing carbon content on phases and properties of IN718
A Baseline Survey of Meiofauna from the Paint Rock River, Northern Alabama
Get PDFhttps://louis.uah.edu/rceu-hcr/1449/thumbnail.jp
Novel Mechanical Test Specimens to Achieve Multiple Constant Elevated Strain Rates Simultaneously
Get PDFhttps://louis.uah.edu/rceu-hcr/1471/thumbnail.jp
A comparative study of tin anodes in carbonate and glyme electrolytes
Get PDFSodium-ion batteries (SIBs) are a focus of novel energy research as they present a sustainable alternative to lithium-ion batteries (LIBs) due to the abundance of sodium and anode materials. Tin is a promising option for anodes in SIBs because of its ability to be used independently or with other metal alloys. The advantage of using styrene butadiene rubber in tin anodes is a source of intrigue for potentially improving the shelf life of anodes and reducing the electrical fatigue on the battery. The study of glyme electrolytes compared to widely used carbonate electrolytes can potentially improve cycling life and the overall performance of the battery. Copper current collectors are the default when it comes to battery anodes but with the low reactivity of sodium with aluminum and its thermal stability, the comparison shows the difference in the charging-discharging capacities which potentially reduces cost. The test cells are subjected to ex-situ material characterization (X-Ray Diffractometry) to study the anode material and electrochemical tests (Galvanostatic Cycling for Potential Limitation, Electrochemical Impedance Spectroscopy and Cyclic Voltammetry) to understand internal electrochemical processes of the batteries. The specific charging-discharging capacities of the cells showed a benefit to aluminum current collectors over copper. An advantage to glyme electrolytes was observed over the 10-cycle period, with significantly lower cell degradation in long-term cycling tests. Impedance responses for short-term cycling showed cell degradation over stepped potentials and fluctuating responses which are accounted to the formation of SEI layer during the initial cycling phase of SIBs. CV results exhibit the evolution of electrochemical activity which are supported by GCPL results
SMS : a smartwatch application suite for mobility assessment
Get PDFRegular assessment of mobility can detect changes in physical health over time and discover underlying health issues. Some of these changes in mobility may indicate an increased risk of falls, which can lead to serious injuries. Identifying mobility changes can help prevent these incidents. Wearable technology can facilitate mobility tests at home and alert caregivers or medical professionals to any irregularities and promote proactive healthcare. Adoption of smartwatches with various built-in sensors like accelerometer and gyroscope creates new opportunities for wearable health monitoring. We developed the Smartwatch Application Suite to evaluate functional mobility using standard mobility tests: Timed Up and Go Test, Thirty Second Chair Stand Test, and Two Minute Walk Test. The application suite was implemented and tested on Samsung Galaxy 4 smartwatch. The applications process signals from inertial sensors, generate mobility parameters, and save all signals and results on the medical server. We present implementation and verification of the application suite
Model-Based Systems Engineering: Evaluating Perceived Value, Metrics, and Evidence Through Literature
Get PDFThe design and testing of a one megaampere pulsed power system
Get PDFThis thesis details the design, construction, and testing of a 1 megaampere (MA) class pulsed power system (PPS) named Sparky. Sparky\u27s creation stemmed from the desire to explore subscale fusion experiments, where a strong correlation between fusion yield and peak current has been observed. The system offers reconfigurability through diverse load attachments enabling it to deliver currents ranging from 105 to 106 amps and cater to various experimental needs. Sparky is a 60 kJ capacitor bank consisting of six 13.5 µF capacitors rated at a maximum charging voltage of 35 kV. Most of our tests, which consisted of discharging Sparky through a dummy load of fifteen parallel 12 cm long cables, were performed at 25 kV. The peak current measured with magnetic field probes was 900 kA, and the circuit rang with a period of 13 µs. A single test into a vacuum chamber, in which a pair of cables was connected between Sparky and the chamber, gave a current of 360 kA and a circuit ringing period of 50 µs for a charging voltage of 25 kV. Development of the subsystems needed for safe control, especially the triggering system, yielded valuable insights into the safe and reliable operation of 1 MA class machines. These insights arose from overcoming various challenges and unforeseen events encountered during testing. This included events such as energetic component failure, unforeseen grounding paths and switching issues. Each of these insights served as a valuable learning experience, informing future iterations and paving the way for a safer and more reliable design