LOUIS University of Alabama in Huntsville
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A Change of Heart: Empire, Envangelism, and the Question of Palestine in the 1930s
No full texthttps://louis.uah.edu/rceu-hcr/1495/thumbnail.jp
Bridging the gap: evaluating a community food distribution center and its impact on food security and access
No full textFood insecurity in the United States (U.S.) is anticipated to remain a significant concern in 2025. In 2023, 13.5% of U.S. households,18 million individuals, experienced food insecurity, an increase from 12.8% in 2022. Given these rising figures, considerable emphasis has been placed on food insecurity as a social determinant of health, functioning as both a contributing factor and a predictor of patient outcomes. Community food distribution agencies are essential in mitigating food insecurity within Madison County. However, few studies have conducted comprehensive evaluations of these community food distribution centers. This assessment evaluated the effectiveness and efficiency of a local food distribution facility by integrating quantitative data from participant surveys and insights from stakeholder interviews. Retrospective data from 2022 to 2024 were analyzed. Data on participant health status, access to care, satisfaction levels, food security, and utilization were collected via survey, including a validated tool. A survey of 320 patrons over four weeks showed that most are unemployed (67%), unmarried (50%), and 80% face food insecurity. Additionally, 40% suffer from chronic conditions and depend on emergency room services. Satisfaction levels were notably high at 97%. The non-profit organization supported its capacity through collaborations with private donors, local businesses, faith-based groups, and the North Alabama Food Bank. The agency’s objective of providing daily food assistance to individuals and families was achieved, serving between 600 and 900 individuals each week, with no eligibility restrictions. Therefore, this evaluation demonstrated the center\u27s role in alleviating participants\u27 food insecurity. It is recommended to perform regular evaluations utilizing consistent methodologies to document data effectively. Expanding services through collaboration with healthcare providers could establish an integrated care system, thereby reducing the dependence on emergency departments that are already overburdened. These insights aim to optimize the center\u27s operations and enhance its ability to serve the community effectively. Keywords: food insecurity, food distribution center, community partnershi
Particle image velocimetry (PIV) measurements of flow characteristics of an unsteady oblique shock wave train
No full textPresented is the development of a Particle Image Velocimetry (PIV) apparatus to quantify the velocity field produced by an oblique shock wave and its reflections in a supersonic/transonic wind tunnel. A flow seeding system is developed using mesquite wood smoke generated in a metal vessel heated with a Vevor TDGC-2KVA variable autotransformer. A pressurized K-bottle drives the smoke through a cooled copper coil and moisture trap to remove contaminants before injection. A dual-pulsed class IV Nd:YAG Big Sky laser with Thorlabs optomechanical components, including two cylindrical lenses, is used to create a pulsed light sheet to illuminate tracer particles. Image pairs are captured using a Phantom V711 camera synchronized using a LaserPulse 610036 synchronizer. PIV measurements are processed in PIVlab software to calculate the velocity field. Measured velocity distributions provide evidence of a turbulent shear layer and a recirculation zone in the vicinity of the oblique shock wave reflection
Influence of hot isostatic pressing and quench rate on aluminum 2139AM, a novel alloy for LPBF
No full textMechanical and microstructural evaluation was carried out on a modified 2XXX series aluminum alloy designed for laser powder bed fusion (LPBF). Aluminum 2139AM samples printed via LPBF were given three distinct heat treatments: baseline T4 with water quench, modified T4 with hot isostatic pressing (HIP) and natural cooling, and modified T4 with HIP and rapid gas quench. This study examines the effects of the experimental HIP treatment with rapid gas cooling on the LPBF samples to increase ductility and assess the potential of the material as an AM replacement for 2024-T4 aluminum parts. Use of HIP processing on 2139AM was successful at increasing elongation at break to 11% compared to 8% without HIP attributed to decreased porosity. Al2Cu precipitates were identified as the principal strengthening precipitate, and precipitate size and dispersion with respect to cooling rates are discussed
Generation of true random numbers with actively stabilized, low dimensional chaos
No full textTrue Random Number Generators (TRNGs) are heavily used and their design often relies on empirical validation from statistical test suites. Reliance solely on empirical observations to estimate entropy rates for cryptographic applications introduces risk, as accurately inferring long-term correlations demands prohibitively large datasets. Empirical validation of TRNGs must be supplemented by strong theoretical backing to justify estimated information theoretic quantities. We present an electronic, hardware TRNG scheme that produces a maximally random output guaranteed from first principles. This TRNG uses a pulse-width based hardware realization of an iterated map that produces physical entropy via low-dimensional chaotic dynamics. The simple dynamics of this source dictate a straight-forward method of bit extraction and permits direct computation of expected entropy rates. By constrast, TRNGs in recent literature often utilize extremely high dimensional chaotic systems or complex feedback for which this type of design and analysis is impossible. This TRNG implementation closely matches its theoretical properties and passes the NIST test suite with 100 million bits
A study of neural compression techniques for image compression using a novel algorithmic lossless-ness criteria
No full textNeural compression methods claim near-lossless reconstruction capabilities based on traditional metrics such as PSNR and MS-SSIM. However, their impact on downstream machine learning tasks remains largely unexplored. This thesis evaluates three state-of-the-art CompressAI models (Cheng2020-anchor, bmshj2018-hyperprior and mbt2018 ) on high-resolution satellite imagery using semantic segmentation as a downstream task benchmark. We introduce the idea of algorithmic losslessness with respect to a downstream machine learning task and use it to benchmark existing state-of-the-art neural compression algorithms. We also compare the results with non-neural compression techniques. We conducted experiments on 11 urban satellite images using two segmentation approaches: MMSegmentation (7 classes) and FLAIR UNet (19 classes). Although all models achieved exceptional reconstruction metrics (0.985-0.996 MS-SSIM), semantic segmentation performance showed significant degradation. MMSegmentation IoU scores decreased by 12-44% between models, while FLAIR UNet demonstrated surprising robustness with only 0.3-6.8% performance loss. Our findings reveal a critical gap between perceptual reconstruction quality and functional task performance. Despite visually identical reconstructions, compression artifacts significantly affected segmentation accuracy, providing crucial insights for AI pipeline compression methods
A study on the nocturnal evolution during PERiLS 2023 IOP3’s supercells to QLCS development
No full textThe Propagation Evolution and Rotation of Linear Storms (PERiLS) field campaign deployed mobile facilities on 24 March 2023 to the Mississippi Delta region. During the intensive operational period, a storm system passed over the PERiLS network that later produced multiple tornadoes in Mississippi and North Alabama. This mesoscale convective system evolved from supercells and merged into a QLCS. This study aimed to document and understand the changes in environmental parameters and associated convective-scale structures as the system moved from initiation over the PERiLS network to North Alabama. The analysis methodology utilizes ground-based assets to ascertain if the MCS was surface driven, the evolution of the thermodynamic environments, and the behavior of the associated mesocyclones/mesovortices. It was found that the system was surface driven, two differing thermodynamic environments evolved to one, and after merging the mesovortices/embedded mesocyclones continually reformed over North Alabama in a linear fashion
Optimizing reliability and design efficiency of liquid rocket engines : a structural margin approach for the early design lifecycle
No full textThis thesis introduces a reliability-based design methodology aimed at mitigating costs incurred during the Test-Fail-Fix cycle for Liquid Rocket Engines (LREs) by addressing the limitations of the standard Factor of Safety (FoS) approach. By integrating a neural network informed by a structural model into a Bayesian framework for uncertainty quantification, the methodology enables the characterization of structural margin and early analysis of failure risks in the design lifecycle. A generalized 3D component geometry is subjected to typical LRE loading environments within Finite Element Analysis software to gather data for stress and strength distributions. These distributions serve as a foundation for assessing the design\u27s adequacy and facilitating informed decision-making. The results highlight improvements in reliability, leading to an effective mitigation technique for the Fail-Fix portion of the Test-Fail-Fix cycle. This research emphasizes the importance of adopting probabilistic approaches in design, ensuring alignment with reliability or mass-saving requirements while reducing overall costs
A comprehensive study on explainable artificial intelligence : a user-centric, cognitive workload, and feasibility-driven analysis
No full textExplainable Artificial Intelligence aims to open up the “black box” of artificial intelligence, thereby improving user trust, decreasing cognitive load, and improving task performance. As the number of machine learning models has grown, so has the number of explanation methods, making it necessary to select the most suitable explanation method for a given context. However, current selection methods often fail to account for all the factors that contribute significantly to an explanation method’s suitability. This thesis proposes a novel methodology for selecting and evaluating explanation methods. Subjective workload, objective performance, and feasibility are considered. This three-pronged “pitchfork” methodology is put to use by evaluating the performance of LIME and Kernel SHAP in a simulated environment where users are tasked with rescuing people. Quantitative data relating to subjective workload, objective performance, and feasibility is captured and analyzed in order to determine the most suitable explanation for the context
