LOUIS University of Alabama in Huntsville
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Sex and eugenics : a memoir in poetry and prose
Get PDFThis memoir is a work of hybrid creative nonfiction which incorporates the genres of essay, memoir and poetry. The memoirist uses multiple forms to address topics from their experience with Turner Syndrome, accessing medical care, medicine and coming to identify as disabled among many aspects of themself. The memoir asks the question; how do we live in a world where both sex and eugenics exist? It starts with the local -- the experience of one individual and expands into something farther reaching – the experiences of others over time. It begins with the perspective and understanding of the author’s own individual and unique experiences with Turner\u27s Syndrome from their childhood diagnosis to adulthood. This focal point in turn leads to the exploration of many intersecting branches of conversation spanning gender roles, disability rights and disability studies, religion, and the specter of eugenics in contemporary society
Molecular Level Chemical Speciation of Biogenic Oxygenated Molecules in the Complex Atmospheric System
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Characteristics of Argon and Nitrogen Purge Gases of L-PBF 316L Stainless Steel
Get PDFhttps://louis.uah.edu/research-horizons/1385/thumbnail.jp
Bridging the Digital Divide: an SLR on Overcoming Barriers to Smart Manufacturing
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Multi-factor mathematical optimization for healthcare referral and facility placement
No full textThis thesis addresses the challenge of optimizing patient referrals in healthcare systems by integrating clinical needs, geographic proximity, and dynamic infection risk. Healthcare referral networks (HRNs), which capture patient transfers between hospitals, serve as the foundation of the study. First, a recommendation algorithm is developed to assign patients by jointly considering clinical compatibility and logistical considerations like travel distance, striking a balance between quality and accessibility. Building on this, a reinforcement learning framework is proposed to dynamically adjust referral strategies for vulnerable patients by incorporating the evolving risk of hospital-acquired infections. Finally, long-term planning is explored through methods that recommend future hospital placements based on projected population demand and referral patterns. These approaches are validated on real-world HRN datasets using metrics of clinical match, efficiency, and infection-aware allocation. Overall, they open up a data-driven route to resilient, equitable, and adaptive referral systems
Initiating a contraception protocol on a North Alabama Historically Black College and University
No full textInitiating a Contraception Protocol at a North Alabama Historically Black College and University (HBCU) Background: Nearly half of pregnancies in the U.S. are unplanned; higher in adolescents, lower income, minority, and single women with poverty rates already twice that of other groups. For black women, the unintended pregnancy rate is disproportionately high in comparison to their white counterparts. These pregnancies promote a significant risk of morbidity and mortality. Black women face barriers associated with inadequate access to reliable and affordable contraception, stereotyping, racial bias, and lack of resource information. These barriers can result in health disparities with a devastating impact on personal and perinatal outcomes. Purpose Statement: To implement a protocol at a college clinic to screen for contraception status and initiate discussions with all female patients on contraceptives promoting uptake PICOT: At a student health clinic, does implementation of a contraceptive screening protocol, compared with no protocol, result in increased contraception discussions and uptake? Methods: The Self-Identified Need for Contraception (SINC) screening algorithm was implemented beginning with, “Would you like to speak with your provider about contraceptives today?”. If yes, a tablet with the electronic educational tool My Birth Control was provided with a comprehensive review of contraceptives in preparation for a discussion with the provider. Shared decision-making was employed to ensure that options were clinically relevant and meaningful to the patient. Contraceptives, prescriptions, and referrals were provided as warranted. A text was sent out two weeks later to assess uptake. Results: 454 women were asked the SINC question. 416 (91.6%) declined the discussion. Reasons: 45% were already on contraception, 33.2% said they were there for another reason, 15.9% did not want contraception, 5.3% said the question did not apply to them, 0.7% were trying to get pregnant, and the rest indicated they were not sexually active 4.56%. Thirty-eight women (8.4%) did agree to the discussion and received education, and 34 (89.5%) initiated contraception. Conclusion/Nursing Implications: Contraception remains a critical topic of discussion and decision. Declining to discuss the subject serves as an obstacle to education and uptake. Methods that ensure a safe environment for the educational discussion should continue to be explored, as women who engage in unprotected sexual practices are at risk of unplanned pregnancies and compromised perinatal outcomes
Prior-bias congruence : case studies across heterogeneous domains towards a unified machine learning framework
No full textMy dissertation argues that effective machine learning arise from the synergistic integration of domain-specific priors and congruent inductive biases. I demonstrate this principle through explorations with diverse data types: considering the inherent spatiotemporal structure of radio frequency signal data, focusing on salient features and underlying distributions within geospatial data, and adapting to the unique vocabulary and statistical properties of specialized scientific corpora. These examples show that aligning model assumptions (biases) with domain knowledge (priors) enhances performance and efficiency. Building on this evidence, I propose an empirical framework, Prior-Bias Congruence (PBC), a conceptual structure aimed at guiding the development of models that systematically leverage domain knowledge. The framework advocates for more principled methods to identify priors and design congruent biases, aiming to create systems that learn effectively from data-driven learning while leveraging the wealth of expert knowledge
A mutually exclusive 3D convolution based time dilation framework for future frame prediction in video streams of natural events
No full textIn this thesis, a framework for predicting future frames from videos of naturally occurring processes will be presented. This is achieved by learning the underlying physical laws of the natural process through the exploitation of spatiotemporal features from the frames of the video. Our framework defines windows containing frames at varying time intervals, which we call dilations, and uses them to predict future frames using a 3D convolutional neural network (CNN). The network architecture accepts six time-dilated windows of input video frames and computes spatiotemporal features for each window maintaining the exclusion of the resulting features across three phases of the network, finally aggregating the features at the end for computing the final predictions. We tested the proposed framework with two datasets; the first is a video of ocean waves and the second one is a video of clouds in the sky
