LOUIS University of Alabama in Huntsville
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Investigating the Potential Resistance of E. coli Against Argon Cold Atmospheric Plasma (CAP) Sterilization Exposure
Get PDFhttps://louis.uah.edu/rceu-hcr/1452/thumbnail.jp
Living in a Rocket\u27s Shadow: Bringing Huntsville\u27s History to Light
Get PDFhttps://louis.uah.edu/rceu-hcr/1456/thumbnail.jp
Flown Flora: NASA\u27s Moon Trees and Public Memory
Get PDFhttps://louis.uah.edu/rceu-hcr/1472/thumbnail.jp
GPU acceleration of kinetic simulations of dust particles in RF plasmas
Get PDFDusty plasmas are characterized by micro to nanometer sized particles interacting with ions and electrons in a plasma. Dust grains interact with each other via Coulomb forces as they acquire electric charge through the collection of electrons and ions. This interaction can generate crystalline structures because of mutual repulsion and produce collective behaviors. Under the influence of gravity, dust crystals are squeezed into a two-dimensional monolayer. In microgravity, three-dimensional crystal structures and a broad range of collective behavior are possible as dust grain interaction forces and plasma effects become dominant. In this study, dusty plasmas were modeled via a continuum description of the plasma coupled with a kinetic description of dust particles where grains are tracked by solving for the grain charge, position, and velocity in response to forces from the plasma background and the interactions between dust particles. Most notably, numerical tools were developed and implemented to conduct the simulations via the parallel processing capabilities of a graphics processing unit (GPU) to vastly accelerate computation speed. The significant speedup in processing allows a two orders of magnitude increase in number of simulated particles within the constraints of computational resources. This speedup begins to allow the computational investigation of collective grain dynamics within the microgravity experiments with a very large number dust particles in an rf plasma reactor. The GPU enabling software modifications and efficiency gains were validated with respect to previous simulations on a gravitational condition
Evaluation of a faculty-coach model to decrease attrition and increase the NCLEX-RN pass rate
No full textIn a prelicensure baccalaureate program at a public state university, a faculty-coach teaching model was implemented for the final semester; however, a formal evaluation of its effectiveness was not conducted. With a focus on accountability and class structure, this model was implemented to decrease final semester attrition and increase the National Council Licensure Examination for Registered Nurses (NCLEX-RN) pass rate. Attrition poses a significant challenge for nursing schools and educational institutions, impacting program success benchmarks and the quality of the nursing workforce. The first-time pass rate on the NCLEX-RN at the University of Alabama Huntsville (UAH) College of Nursing has been inconsistent for over a decade, with percentages as low as 79%. The National Council of State Boards of Nursing (NCSBN) reported an all-time low percentage in 2022 of success for first-time test takers for the previous 10 years (NCSBN, 2022). This low pass rate not only affects students, but directly affects the profession of Nursing as a shortage of nurses still plagues our society. A multi-tiered intervention was implemented in 2020 at UAH focused on faculty coaching and customized remediation. Though regular evaluations of a nursing program—including its curriculum, teaching methods, and assessment strategies—facilitate data-driven decisions for continuous quality improvement, this last-semester program was never formally evaluated for efficacy. A program evaluation based on Daniel Stufflebeam’s context, input, process, and product (CIPP) evaluation model was implemented to assess the impact of a faculty coach model on final semester attrition and NCLEX-RN pass rate. Though previous programmatic and curricular efforts must be acknowledged, after the implementation of the faculty coach model, final semester attrition dropped significantly, reaching zero by 2023. The NCLEX-RN first-time pass rates also improved, consistently surpassing 95% and nearing 100% in recent years, highlighting the model\u27s role in enhancing student outcomes
