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The Effect of Print Parameters on Permeability and Surface Roughness of 3D Printed Sand Cores and Resulting Castings
No full textThis experiment utilized a modified 3D selective powder deposition sand printer to print cores using shell sand. The cores were adjusted on the printer by the print factors: object layer thickness, fine track overlap distance, and fat track rarity. Each factor had an upper and lower value, with a midpoint to detect curvature and estimate variance. Both layer thickness and overlap distance had an upper value of 0.75 mm and a lower value of 0.25 mm, while the rarity had 4 and 2 respectively. The full factorial results in 11 printers per standard run order, which was repeated 3 times per orientation. These sets of cores were printed in two different orientations, XY and YZ, which results in a total of 66 cores produced. The printer parameters were evaluated to investigate any significant effect on the response variables: permeability, weight, and surface roughness. Select cores were cast to investigate the relationship between the core and casting surface roughness. All data was analyzed using an analysis of variance statistical method to indicate any relationship between the factors and response variables. The response variables were also compared to indicate if any correlation was present. The most significant factor for surface roughness was object layer thickness. Permeability was not significantly affected by any factors in the XY print orientation. There was no correlation between the surface roughness of the core and its resulting casting.Engineering Technolog
The Effects of Soft Tissue on the Dismemberment of Bones Caused by Sharp Force Trauma
No full textNo abstract prepared.Anthropolog
Effectiveness of Ottuki Korean parenting program on parenting practice and mental health outcomes
No full textThe purpose of this study was to investigate the effect of the Ottuki Korean Parenting Program on Korean American parenting practices, parental confidence, and child mental health.Nursin
Motor Fault Diagnosis Across Variable Power Using Deep Learning
No full textElectrical motors are crucial machines in industrial operations. Their unexpected failures can lead to downtime and operational stop. This research presents a general fault diagnosis system based on deep learning, specifically designed to classify motor faults across a wide range of power levels. The objective is to create a robust and generalizable model that can accurately identify faults under different fault scenarios. Time-series data including RPM, torque, power, vibration, rotor and stator currents, flux linkages, and terminal voltages—was generated using MATLAB Simscape simulations of induction motors with power ratings of 4 kW, 8 kW, 15 kW, 37 kW, 75 kW, 110 kW, and 150 kW. Each motor configuration was simulated under five operating conditions: normal, broken bearing, broken rotor bar, stator winding short circuit, and voltage imbalance, resulting in 35 different motor-fault combinations. All simulations were sampled at 42 kHz over 10 seconds, yielding a dataset of approximately 14.7 million samples.
A transformer deep learning model was trained with this high-resolution dataset. The model consists of a linear embedding layer, stacked encoder blocks, 8 multi-head self-attention, and a global average pooling layer, followed by a classification head for thirty-five classes. The model was trained with early stopping and optimized using the Adam optimizer on a GPU-accelerated HPC platform. Results demonstrate excellent classification performance, achieving 100% accuracy on both validation, training and test sets. The proposed framework effectively captures complex fault patterns across power levels and offers reliable solutions for industrial motor health monitoring systems.Engineerin
Resilience as a mediator between social determinants of vulnerability and mental health of residents in rural East Texas communities
Full text linkVulnerability and Mental Health Disparities Rural populations, including in Texas, face greater mental health challenges and disparities than urban ones. Social determinants of vulnerability— demographic, socioeconomic, and structural factors— increase susceptibility to adverse events and hinder access to resources essential for recovery and mental well-being (Carpenter-Song & Snell-Rood, 2017). The Power of Resilience Resilience—the ability to cope, adapt, and thrive across phases of challenge (Ekren et al., 2024)—can buffer stress and mediate its effects on mental health (Akil & Nestler, 2023), especially in disaster recovery contexts. Research Objective We aimed to access health information from hard-to reach rural populations and provide evidence for the critical role of resilience in shaping mental health outcomes, especially in resource-constrained settings.Translational Health Research Cente
Oral history interview: Larry Deaver
Full text linkTranscript file (.pdf) and closed captioning available.Video interviewing Larry 'Joe' Deaver's son, Alan Deaver. Alan shares his dad's story, his time in the army and how family was the most important thing to him
Disclosing AI Involvement in E-Commerce Product Descriptions: Effects on Consumer Perceptions and Purchase Intention
No full textNo abstract prepared.Marketin
Children's Perceptions of Parental Violence: A Quantitative Analysis
No full textNo abstract prepared.Sociolog
Radically valuable: How delivering & receiving candid feedback increases professional value proposition
No full textNo abstract prepared
Teacher Burnout, Neoliberalism, and the Fight for a Thriving Education Body
No full textNo abstract prepared.Counseling, Leadership, Adult Education, and School Psycholog