AUETD (Auburn University)
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K-12 Educators’ Comfortability and Perception of Artificial Intelligence
Get PDFThe purpose of this study was to understand if a professional development course affected K-12 teachers’ comfort with AI, perceptions of AI, and access to resources. Further, the study sought to understand if there were relationships between perceptions of AI and comfort with AI,
and access to resources and comfort with AI, as well as if demographic variables affected participants’ comfort with AI. Secondary data were obtained in partnership with the Auburn University Biggio Center for the Enhancement of Teaching and Learning. In the first analysis, correlations were run to determine if there was a relationship between perceptions of AI and comfort with AI. This analysis indicated a statistically significant relationship between perceptions of AI and comfort with AI. In the second analysis, correlations were run to determine if there was a relationship between access to resources and comfort with AI. This analysis indicated a statistically significant relationship between access to resources and comfort with AI. In the final analysis, independent samples t-tests, a factorial ANOVA, and correlations were run to determine whether demographic variables influenced AI comfort. This analysis indicated no statistically significant relationship between any demographic variable and comfort with AI, with only the number of years teaching getting close to the significant level. This student represents the first step of understanding the effect of AI in K-12 education and the need for professional development resources to ensure the effective implementation of AI in the classroom
The effect of solder alloys and surface finishes on the thermal cycling reliability of different electronic packages
No full textElectronic products have become essential in modern society, powering everything from communication devices to critical infrastructure. Eutectic SnPb (Tin-Lead) solder has been used in electronics since the early days due to its excellent mechanical and electrical properties, making it a reliable choice for soldering applications. However, by the late 20th century, the harmful effects of lead were recognized, prompting a shift towards lead-free electronics. In response to these issues, the industry engaged in extensive research to find suitable alternatives, ultimately leading to the development of near-eutectic alloys based on tin (Sn), silver (Ag), and copper (Cu), known as SAC solder alloys.
In everyday applications, solder joints endure thermal and mechanical stresses, causing the microstructure to evolve and the mechanical properties to degrade, ultimately leading to component failure. Over time, solder materials have shifted from traditional SnPb alloys to lead-free alternatives, later incorporating dopants like Bismuth (Bi), Antimony (Sb), and Nickel (Ni), which have been shown to enhance thermal and mechanical performance. While the literature has examined solder alloys and their function, the reliability of solder joints, particularly in an extensive context based on joint shape and surface finishes under thermal cycling conditions, remains unexplored. This research focuses on the reliability analysis of a series of electronic packages (CABGA, CVBGA, MLF, and SMR) subjected to thermal cycling using various solder alloys, and surface finishes. The study is designed to evaluate how these factors influence the characteristic life and failure mechanisms of solder joints for different components under harsh environmental conditions. A combination of traditional methods, such as Weibull analysis, along with advanced machine learning techniques, is employed to identify the most critical factors affecting reliability. Furthermore, a novel approach based on the Maximum Entropy Principle is developed and tested to create a more accurate predictive model. Moreover, this research develops and utilizes a new algorithm and software to monitor data in thermal cycling tests. This research aims to contribute to understanding solder joint reliability and provide improved models for predicting the lifetime of electronic components in industrial applications
Exploring Students’ Adoption of Generative AI for Apparel Design
No full textAcademic institutions are increasingly integrating Generative AI into apparel design curricula to align with industry demands. This exploratory study examines the factors influencing students’ adoption of Generative AI for apparel design by drawing on multiple theoretical perspectives, including Diffusion of Innovation Theory, Hedonic System Acceptance Model, and Motivational Theory, and incorporating multiple variables to align with the study’s exploratory objectives. This study investigates (a) the direct effects of fashion students’ personality traits (AI learning anxiety, AI anxiety in job replacement, AI fear, curiosity, tech-optimism, and tech self-efficacy) on pragmatic factors (perceived usefulness, perceived sustainability, extrinsic motivation related to the use of Generative AI in apparel design) and hedonic factors (perceived ease of use, perceived enjoyment, perceived creativity, intrinsic motivation related to the use of Generative AI in apparel design). Additionally, it examines (b) the direct effects of the pragmatic and hedonic factors on their intentions to use and willingness to learn this technology for apparel design. Lastly, this study explores (c) the mediating effects of the pragmatic and hedonic factors in the relationship between personality traits and adoption outcomes, including intention to use and willingness to learn Generative AI for apparel design. Data were collected through an online questionnaire administered to 134 fashion students, with 89 valid responses analyzed. Multivariate regression analyses revealed that tech-optimism was the most significant personality trait, positively influencing both pragmatic factors (i.e., perceived usefulness, extrinsic motivation) and hedonic factors (i.e., perceived ease of use, perceived enjoyment, perceived creativity, intrinsic motivation). AI anxiety in job replacement significantly and negatively influenced pragmatic factors (i.e., perceived sustainability, extrinsic motivation) and hedonic factors (i.e., perceived enjoyment, perceived creativity, intrinsic motivation). Tech self-efficacy significantly positively predicted only one hedonic factor, perceived ease of use. Among pragmatic and hedonic factors, extrinsic motivation, a pragmatic factor, had the most significant positive influence on both adoption outcomes: intention to use and willingness to learn Generative AI for apparel design. Perceived usefulness, a pragmatic factor, had a significant positive influence only on intention to use, while perceived enjoyment, a hedonic factor, had a significant positive influence only on willingness to learn Generative AI for apparel design. Mediation analyses through Hayes PROCESS macro Model 4 demonstrated that extrinsic motivation, a pragmatic factor, partially mediated the relationship between two personality traits, AI anxiety in job replacement and tech-optimism, and both adoption outcomes, including intention to use and willingness to learn Generative AI for apparel design. Perceived usefulness, a pragmatic factor, also partially mediated the relationship between tech-optimism and intention to use Generative AI for apparel design. A hedonic factor, perceived enjoyment, played a significant mediating role in the relationships between two personality traits, AI anxiety in job replacement (partial mediation) and tech optimism (full mediation), and willingness to learn Generative AI for apparel design. Theoretically, this study contributes by integrating multiple frameworks to provide a comprehensive understanding of students’ adoption of Generative AI in apparel design education, emphasizing the distinct roles of personality and both pragmatic and hedonic adoption pathways. Practically, the findings suggest that fashion educators should foster a supportive environment that reduces AI-related anxiety, particularly regarding job replacement, while enhancing students’ tech-optimism, extrinsic motivation, perceived usefulness, and enjoyment related to the use Generative AI in apparel design
Material Characterization of Doped Lead-Free Solders at High Strain Rates and Extreme temperatures and Prediction of Mechanical Properties
No full textThe reliability of solder joints remains a critical concern in advanced electronic packaging, particularly under harsh service conditions where high strain rates, extreme temperatures, and prolonged thermal aging accelerate material degradation. This dissertation presents a comprehensive investigation of the mechanical behavior, thermal stability, and constitutive modeling of SAC-R, QSAC10, and QSAC20 solder alloys. High strain rate tensile testing (10–75 s⁻¹) was conducted across a broad temperature range (–65 °C to +200 °C) using an impact hammer-based setup, while long-term thermal aging studies were performed at 50 °C for up to 360 days. Experimental results demonstrate that strain rate increases ultimate tensile strength (UTS) and elastic modulus (E) through strain-hardening, whereas elevated temperatures reduce these properties due to thermal softening. Thermal aging leads to progressive mechanical degradation, though doped alloys—particularly QSAC10 and QSAC20—exhibit significantly better property retention compared to undoped SAC-R, with QSAC20 consistently achieving the highest strength values.
To model these behaviors, the Anand visco-plasticity framework was calibrated using experimental stress–strain data, capturing both temperature and strain-rate sensitivity. The study revealed that thermal aging alters key Anand parameters (A, ŝ, h₀) while leaving others (Q/R, ξ) relatively unchanged, with doped alloys showing slower parameter evolution and greater thermal stability. The strong agreement between experimental data and Anand model predictions validates its applicability for finite element simulations of solder joint reliability. By correlating Bi content with mechanical properties and aging trends, predictive frameworks were also established, enabling the extrapolation of performance for untested solder compositions without requiring extensive experimental campaigns.
Overall, this research demonstrates the pivotal role of Bi doping in enhancing solder alloy performance and establishes an integrated experimental–computational framework for predicting solder joint behavior under dynamic and aging conditions. The findings contribute directly to the design of reliable, high-performance electronic systems for aerospace, automotive, defense, and industrial applications, where long-term durability under extreme environments is paramount
Investigation of 1-Trichloromethyl-1,2,3,4-tetrahydro-β-carboline (TaClo)-Induced Neurotoxicity in the Zebrafish Models (Danio rerio)
No full textEnvironmental pollutants have been linked to neurotoxicity and are proposed to contribute to neurodegenerative disorders. Trichloroethylene (TCE) has been identified as an environmental contaminant and a potential risk factor for neurologic diseases. In recent studies, 1-Trichloromethyl-1,2,3,4-tetrahydro-β-carboline (TaClo), one of the metabolic compounds of TCE, has been implicated as a potent neurotoxicant in TCE-induced neurotoxicity. In addition, TaClo has been associated with Parkinson’s disease (PD) due to its neurotoxic effects and structural resemblance to 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). The zebrafish model offers a high-throughput and cost-effective platform for chemical screening and toxicity assessment and is widely used in the study of neurodegenerative diseases. While previous studies have shown that environmentally relevant concentrations of TCE induce neurotoxic effects in developing zebrafish, the specific role of TaClo in zebrafish neurotoxicity remains largely unexplored. Therefore, this dissertation aims to investigate TaClo-induced neurotoxicity and to compare its effects with those of MPTP in zebrafish models. In addition, we sought to elucidate the underlying mechanisms of TaClo toxicity, evaluate its long-term effects following early-life exposure, and explore its potential role in contributing to broader diseases. Using zebrafish models, we demonstrated that early-life exposure to TaClo induces neurotoxicity closely resembling that of MPTP at the larval stage. Moreover, our study demonstrated that early-life exposure to TaClo and MPTP results in long-term neurotoxicity that persists into adulthood. Finally, transcriptomic analysis revealed that TaClo and MPTP utilize distinct molecular pathways to mediate neurotoxicity and contribute to various systemic diseases. These findings underscore the value of zebrafish as a model for studying environmental neurotoxicants, support the potential role of TaClo in neurodegenerative diseases, and emphasize the need to address TCE contamination to mitigate long-term public health risks. Overall, this dissertation advances our understanding of the neurotoxic effects of TaClo, a metabolite of the widespread environmental contaminant TCE, and its similarities and differences with the established neurotoxin MPTP
Assessing hurricane-driven forest changes using satellite-based lidar and multispectral imagery
No full textCoastal forests in Alabama and Florida are increasingly vulnerable to storms and hurricanes, which can cause widespread canopy loss, decline in forest structure, and long-term disruption of ecosystem services. Accurate and scalable methods for assessing hurricane-driven forest damage are essential for understanding ecological impacts, informing restoration efforts, and guiding long-term forest resilience planning. This thesis addresses the pressing need for high-resolution, spatially comprehensive tools to monitor hurricane-driven forest changes by leveraging spaceborne lidar, multispectral imagery, and machine learning techniques. In the first study, we evaluated the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) land and vegetation height product, or ATL08, at two resolutions (100 m segment and 20 m sub-segment), to assess pre- and post-hurricane changes in canopy structure using repeat-track observations within the damage extent of Hurricane Sally (September 2020). Strong agreement was observed between ATL08-derived 98th percentile canopy height (RH98) and airborne lidar data RH98, particularly at the 20-meter sub-segment scale (RMSE = 3.44 m, r = 0.80). Structural analyses revealed significant height reductions in mature trees (−1.51 m) and increases in understory vegetation (+0.77 m), reflecting both canopy damage and regeneration. Building upon these findings, the second study entailed the production of pre- and post-hurricane canopy height maps, changed canopy height maps, and associated thematic transitions. Machine learning algorithms, primarily Random Forest (RF) and Extreme Gradient Boosted (XGB) regression models, were trained using ICESat-2 data, Sentinel-2 imagery, Landscape Fire and Resource Management Planning Tools, National Land Cover Database derived predictors, and topographic variables. The integration of ATL08 lidar with high-resolution imagery, such as data from Sentinel-2, presents an opportunity to develop spatially explicit hurricane assessment tools. The RF showed superior performance (R² = 0.44, RMSE = 4.30 m) compared to XGBoost (R² = 0.41, RMSE = 4.76 m), and predicted pre-hurricane canopy height maps showed strong agreement with an existing ICESat-2-derived 2020 canopy height product (r = 0.57, RMSE = 3.49 m). Landcover change analysis revealed shifts from evergreen forests to herbaceous, scrub, and barren classes, and woody wetlands to emergent herbaceous wetlands with mean canopy height losses of 2.3 to 5.2 m. Canopy cover analysis showed dense (>60%) and sparse (<30%) cover experienced severe canopy height loss (up to 8.3 m), while moderate covers (30–60%) were resilient. Together, these studies demonstrate the potential of repeat-track ICESat-2 observations, synergistic use of multi-temporal ICESat-2 and Sentinel-2, and machine learning–driven canopy modeling to assess changes in forest structure and hurricane-driven canopy changes. This integrated approach provides a robust, scalable methodology for post-hurricane forest damage assessments, as well as informing adaptive strategies in hurricane-prone coastal forest ecosystems
Harnessing Stylistic Influence in NLP: A Gateway to Deeper Language Understanding
No full textThe concept of style permeates all aspects of human interaction, shaping how we interpret, express, and engage with the world. Stylistic uniqueness is what distinguishes individuals and influences how they solve problems. In the context of language, style not only reflects personal idiosyncrasies but also plays a pivotal role in how meaning is conveyed and understood. This power of stylistic uniqueness is evident in its diverse applications. In digital forensics, identifying stylistic fingerprints can be lifesaving, helping trace authorship in critical situations. In education, it plays a key role in preventing plagiarism, ensuring originality, and enforcing copyright in scholarly work. These applications demonstrate the powerful role stylistic analysis can play in maintaining ethical standards and security in digital domains.
Beyond its original applications, the growing role of stylistic influence in NLP has reshaped many content-based tasks into profile-based approaches. For example, hate speech detection is no longer just about labeling content but now focuses on evaluating an author's propensity to spread harmful narratives. Methodologically, the field has progressed beyond classical and statistical models toward more advanced approaches. In our prior work, we explored the integration of stylistic signals with pretrained language models using novel architectures, including graph-based methods designed to better capture the structural and relational properties of style.
Despite these advances, two core challenges remain largely overlooked. First, many existing models lack support for class-incremental learning—an essential capability for adapting to newly emerging authors without full retraining. Second, they often fail to capture subtle yet distinctive stylistic cues that separate closely related authors. In this proposal, we address both challenges through a series of recent studies. One line of work introduces the problem of class-incremental learning in stylistic modeling. Building on that, we propose a new framework that combines metric learning with stylistic-semantic representation to enable continual learning while enhancing fine-grained author discrimination. This approach outperforms state-of-the-art methods across several authorship attribution benchmarks.
Finally, as large language models (LLMs) become more prevalent, it is crucial for these models to develop their own stylistic distinctions to safeguard intellectual property rights and prevent misattribution. To this end, we explore Fermi problems—a unique reasoning-based benchmark—to investigate how LLMs can cultivate distinctive stylistic traits in their problem-solving capabilities
Characterization of a novel macrocyclic inhibitor of Lassa virus
Get PDFLassa virus is a highly pathogenic virus belonging to the family Arenaviridae. It is the causative agent of Lassa hemorrhagic fever and is a significant threat to global public health. Lassa virus is endemic to West Africa but has been imported into other countries on multiple occasions. Though most cases are mild, 20% of infections cause significant disease requiring hospitalization. There are currently no approved medical countermeasures against Lassa virus. Current treatment for individuals infected with Lassa virus include supportive care and the controversial off-label use of the antiviral ribavirin.
The objective of this dissertation was to assess and characterize the mechanism of action of a novel compound, Mac12895623 (Mac128), with potent anti-Lassa properties. Mac128 belongs to a class of compounds called macrocycles. They are large, circularized compounds, and can combine many desirable characteristics of small molecules with unique structural features. Unlike other classes of small molecule compounds, macrocycles may bind motifs that were thought to be ‘undruggable’, opening the door to new avenues in drug discovery.
Chapter I serves as a literature review to introduce Lassa virus and elaborate on current medical countermeasures, the development of antivirals against high consequence pathogens, and macrocycles as antiviral therapeutics. Chapter II, outlines the materials and methods used for the research conducted herein. The results of the research are presented in Chapter III in which a family of structurally related macrocyclic compounds was identified to inhibit Lassa virus using a high-throughput screening campaign composed of almost 60,000 compounds. The most promising compound, Mac128, was assessed in a series of in vitro assays to characterize the inhibition of other Lassa virus clades and pertinent New and Old-World arenaviruses. We found Mac128 possessed Lassa-specific inhibition and various clades demonstrated differences in susceptibility. Mechanistic assays were used to assess the macrocycles effect on entry, replication and budding. These experiments revealed Mac128 inhibited viral replication and likely targets the viral polymerase.
There are currently two drugs and two experimental compounds in clinical trials to treat Lassa hemorrhagic fever. Though these candidates are promising, there is still a need for medical countermeasure development against Lassa virus. By characterizing a novel compound class, this work aims to further the understanding in which this deadly pathogen can be inhibited and contribute to the development of efficacious therapeutics against Lassa virus
Improved Momentum-Integral Framework for Modeling Viscous and Thermal Boundary Layers
No full textThis work advances the classical Kármán–Pohlhausen (KP) momentum-integral method by applying an improved nearfield formulation, developed by Majdalani and Xuan, to several canonical problems. This formulation resolves a century-old inconsistency in boundary-layer predictions. The original KP formulation, while foundational, imposed a mathematically convenient but physically inaccurate condition on the second derivative of the velocity profile at the boundary-layer edge. Building on recent work by Majdalani and Xuan, this study adopts a corrected fourth-order polynomial representation (MX4) that eliminates this constraint, significantly improving the accuracy of both viscous and thermal boundary-layer predictions. The enhanced MX4 profile is first applied to external flows over circular cylinders under potential farfield conditions and then under real Hiemenz flow conditions. Compared to traditional KP profiles, the updated method yields more accurate estimates of separation angles, wall shear, and heat transfer metrics. Further improvements are demonstrated by incorporating the Hiemenz flow as a more realistic farfield approximation. This adjustment enhances the agreement with experimental data and computational benchmarks across a wide subcritical Reynolds number range. To generalize the method for more adaptable geometries, the analysis is extended to elliptic cylinders in crossflow with arbitrary aspect ratios. The study shows that increasing the aspect ratio delays separation and reduces both skin friction and drag, with the MX4 model performing especially well for slender configurations. Given the absence of consistent farfield models for non-circular geometries, new empirical velocity profiles are developed from RANS simulations, enabling practical implementation across a broader range of Reynolds numbers. The framework is further adapted to internal swirling flows within a cylindrical tube, a configuration motivated by propulsion applications such as the Vortex Combustion Cold-Wall (VCCW) chamber. By solving the axial, tangential, and thermal momentum-integral equations as a coupled system, the model captures the complex interplay between the swirl and Reynolds numbers. Results highlight the competing effects of swirl-induced thickening and Reynolds-driven thinning of the boundary layers, with corresponding impacts on wall shear and heat transfer. Overall, this work demonstrates the effectiveness and versatility of the momentum-integral approach in capturing key boundary-layer characteristics and behavior. The resulting methodology serves as a practical and efficient alternative to full numerical simulations for a variety of external and internal flow configurations relevant to engineering design
Age and Growth of Crappies in Alabama Reservoirs and Evaluation of the Statewide Minimum-Length-Limit
No full textBlack Crappies Pomoxis nigromaculatus and White Crappies P. annularis in Alabama are currently regulated with a 229-mm minimum length limit (MLL) and a 30 fish/person/day bag limit. With the recent increased angler use of technologies that may allow anglers to target crappies more efficiently, evaluation of current regulations was warranted. We used electrofishing to collect 6,125 crappies between 2022 and 2024. We enumerated, measured morphometrics, sexed, and aged all fish. We estimated growth, mortality, and recruitment in all reservoirs and these data were variable among reservoirs. We developed reservoir-specific age-structured Beverton-Holt equilibrium yield models. We ran models using four MLLs to observe predicted population effects with multiple regulation scenarios, which included an assumed “no MLL.” Reservoirs characterized by fast growth and low conditional natural mortality had the most favorable tradeoff associated with a MLL that maximized yield, limited decreases in harvest, and increased the number of memorable sized crappies. In five reservoirs, we split up data collected from the upper riverine versus the lower, main lake sections to analyze differences in population data between sections. We found fish collected did not reach the same size in most upper sections, compared to the lower section, possibly indicating growth overfishing in upper sections of these reservoirs, but could also be caused by underlying factors such as variation in habitat, density dependence, species interactions, and sampling bias. These data will be useful for managers to evaluate multiple regulation scenarios among crappie populations in different reservoirs. These data, coupled with angler preferences, would be useful for evaluating the most favorable regulation(s) that also have a biological underpinning