The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. https://etd.auburn.edu:443 2026-07-16T23:16:28Z 2026-07-16T23:16:28Z Black, Hunter https://etd.auburn.edu/handle/10415/10441 2026-07-16T19:37:14Z 2026-07-16T00:00:00Z

Mechanistic Insights and Process Optimization of Acid-Treated Soybean Hulls as Aquatic Feed Binders Black, Hunter This work utilizes soybean hull (SBH) as a low-cost feedstock for the development of an acid-treated aquatic feed binder based on whole biomass utilization. Currently, SBH is generated in large amounts (34.17 - 42.72 million metric tons annually) with most of its utilization being limited to animal feed applications, landfilling, and incineration. A single-stage acid treatment was applied to untreated SBH at both 10% and 50% solids, pH = 1.8, and different treatment times (i.e. 0.5-hour, 1-hour, 1.5-hour, and 2-hour) to evaluate changes in physical and chemical properties. This acid treatment results in a reduction of neutral sugars and pectin content due to the respective degradation and extraction into ethanol (EtOH) extractives during the chemical composition analysis. It also leads to a 40-60% increase in carboxylic acid content, a 70-90% increase in water solubility, and up to a 23.63% reduction in geometric mean particle diameter. These properties improved with increasing treatment time, with the 1.5-hour and 2-hour treatment at 50% solids exhibiting the best properties. However, prolonged treatment also promoted agglomeration, requiring additional size reduction during drying to avoid compromising binder performance during incorporation into fish feed production processes. Given the high cost associated with transporting wet binder, the effect of productions solids content and drying methods, including oven drying at different temperatures and freeze drying, were evaluated to identify optimal processing conditions. Finally, a technoeconomic analysis (TEA) was conducted for the 1.5-hour 50% solids binder production process to assess the economic feasibility and profitability of a batch plant producing 6-ton dry binder per day. The minimum sales price of the SBH binder was determined and compared against common commercial aquatic feed binders such as carboxymethyl cellulose (CMC), wheat gluten, and corn starch.

2026-07-16T00:00:00Z Moncrief, Sarah https://etd.auburn.edu/handle/10415/10440 2026-07-15T21:10:26Z 2026-07-15T00:00:00Z

An Exploration of the Lived Experiences of Principals with School Counseling Backgrounds Moncrief, Sarah Research surrounding school principals indicates that the candidate pool is shrinking for this vitally important position. School counselors are great candidates to fill this position. Their experience as a counselor helps them solidify strong leadership skills and abilities that can contribute to a successful school principalship. The purpose of this phenomenological study was to explore the lived experiences of school principals with school counseling experience. In particular, how did these backgrounds influence factors pertaining to being purpose and focus oriented, enacting ethical and equitable responsibility, acting as instructional leaders, collaborating and interacting with school community and stakeholders, creating and maintaining professional work relationships, and interpreting data for continuous improvement. Five participants who are currently or who have been school principals and also school counselors were interviewed and completed a follow up survey. Throughout the data collection and analysis process, five themes emerged. The findings of this study show that these individuals place a priority on strong professional relationships, keep students at the forefront of decision making, focus on whole child education, believe their time as a school counselor greatly prepared them for their principal positions, and always keep a growth mindset.

2026-07-15T00:00:00Z Anyi, Li https://etd.auburn.edu/handle/10415/10439 2026-07-15T21:07:37Z 2026-07-15T00:00:00Z

Physics-informed Hierarchical Learning for Defect Characterization and Fatigue Life Prediction in Metal Additive Manufacturing Anyi, Li Understanding and accurately predicting the fatigue life of additive manufactured (AM) metal parts remains a pressing challenge for their reliable adoption in safety-critical applications such as aerospace, energy, and biomedical systems. In laser powder bed fusion (L-PBF), process-induced volumetric defects are one of the primary factors governing crack initiation and fatigue failure. Traditional fatigue assessment relies on destructive and time-consuming fatigue testing, resulting in fatigue data that is expensive and scarce. Consequently, individual organizations often possess insufficient data to develop robust predictive models. While collaborative learning across organizations provides a potential solution, concerns about heterogeneous inspection capabilities and data privacy hinder effective data sharing and joint model development. Moreover, most existing studies focus on uniaxial loading conditions, whereas engineering components are usually experienced complex multiaxial stress states. These challenges span various levels of fatigue assessment, ranging from defect-level characterization and specimen-level fatigue life prediction to organization-level collaborative learning and multiple-defect level multiaxial fatigue behavior understanding. To address these challenges, this research develops a physics-informed hierarchical learning framework for defect characterization, fatigue life prediction, and collaborative learning in AM. The framework progressively enhances defect-fatigue understanding and prediction across multiple hierarchical levels through a series of physics-informed data-driven methodologies. Specifically, four methodologies are introduced: (1) An integrated data-driven analytical framework using kernel support vector regression and interpretable model-agnostic methods for defect criticality analysis and fatigue life prediction of L-PBF 17-4 PH stainless steel parts. As the foundation of the hierarchical framework, this defect-level methodology enhances the understanding of defect-fatigue relationships in a data-driven manner and quantitatively analyzes the importance of defect features to fatigue life. (2) A multimodal transfer learning (MMTL) framework using process-defect-loading data for defect classification and nondestructive fatigue life prediction of L-PBF Ti-6Al-4V parts. At the specimen level, this framework leverages knowledge learned from a pre-trained model with abundant process and defect data in the source task to predict fatigue life nondestructively with limited fatigue test data in the target task. (3) A personalized federated transfer learning framework using conditional optimal transport (FedCOT) for collaborative predictive modeling across organizations with heterogeneous inspection capabilities. At the organization level, this framework enables ``target" organizations with limited features to benefit from ``source" organizations with sufficient features in terms of prediction performance, while preserving data privacy through a central server. (4) A physics-informed multiple instance learning (PhysMIL) framework for multiaxial fatigue life prediction of AM metal parts. At the multiple-defect level, this framework integrates the critical plane mechanics with defect representations from multiple defects within each specimen and learns their contributions to fatigue failure via attention-based aggregation and physics-informed regularization on defect characteristics and fatigue damage mechanisms. It improves both prediction accuracy and interpretability under complex multiaxial loading conditions. These methodologies establish a unified physics-informed hierarchical learning framework that advances the understanding of process-defect-fatigue relationships, enables nondestructive and data-efficient fatigue life prediction, supports privacy-preserving collaboration across organizations, and extends predictive modeling to realistic multiaxial loading scenarios. They provide practical tools for improving the reliability and qualification of AM components and can be utilized in other engineering domains involving limited, heterogeneous, and privacy-sensitive data.

2026-07-15T00:00:00Z Joshi, Puspa Raj https://etd.auburn.edu/handle/10415/10438 2026-07-15T21:04:31Z 2026-07-15T00:00:00Z

Optimizing Log Truck Transportation: Analyzing Route Safety and Transportation Efficiency Joshi, Puspa Raj This study assessed the feasibility of rerouting log trucks away from state and county roads to interstate highways with modest increase in Gross Vehicle Weight (GVW) limit of 88,000 lbs. which is the current GVW for state and county roadways in Alabama. Interstate GVW is limited to 80,000 lbs. Optimal log truck routes were simulated using ArcGIS Pro Closest Facility network analysis method. Findings suggest that rerouting log trucks to interstates would reduce their interactions with road intersections and traffic stops by 52.9% and 36.6% as well as a 48% reduction in rural primary miles traveled; a road segment associated with a higher proportion of fatal log truck crashes. Furthermore, interstate travel reduces one-way travel time by 14%, increasing daily haul capacity and revenue, particularly when interstate GVW limits were raised to 88,000 lbs., generating an additional yearly revenue of $27,881.61. Pavement damage costs are also estimated to be 45.9% lower on interstate highways compared to state and county roads, suggesting increased interstate access, coupled with higher GVW limits, could substantially improve safety and operational efficiency in timber transportation in Alabama and beyond.

2026-07-15T00:00:00Z