17179 research outputs found
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Effect of Organic Amendments and Biostimulants on Zucchini Yield and Fruit Quality Under Alkaline Conditions
No full textSoil amendments can enhance soil and plant health; however, limited research has addressed their effects on soil health and crop productivity in alkaline soil. This study investigated the effects of various soil amendments and biostimulants by the Haney Soil Health Test, plant sap analysis, and Cucurbita pepo cv. ‘Dunja’ yield and quality. Treatments included unamended soil (T1) and applications of Humisoil® (T2), Humisoil with biochar (T3), wood vinegar (T4), Ensoil algaeTM (T5), and Humisoil with biochar and basaltic rock dust (T6). Compared to T1, T6, T5, T2, and T3 increased yield by 107%, 87%, 86%, and 52%, respectively. Regarding total fruit number per plant, T2, T6, and T5 outperformed T1 by 42%, 37%, and 37%, respectively. Additionally, T6 decreased Na concentration by 59% in the sap of young leaves and 50% in old leaves compared to T1. Compared to T1, T2 also reduced Na concentration in the sap of old leaves by 63%. For Cl, decreases of 30%, 16%, and 24% in old leaves were observed in T2, T4, and T6 treatments, respectively. These findings highlight the potential of biostimulants and soil amendments to improve zucchini yield and quality while improving soil health in alkaline soils.Agricultural Science
Exploring College Students' Perceptions of Receiving Academic Help
No full textNo abstract prepared.Curriculum and Instructio
ROVON: An Ontology for Supporting Interoperability for Underwater Robots
No full textUnderwater robotics produces diverse and complex streams of sensor, image, video, and navigational data under challenging environmental conditions, creating obstacles for seamless integration and interpretation. This paper introduces ROVON (Remotely Operated Vehicle Ontology), a semantic framework designed to enhance interoperability and reasoning in underwater operations. While ROVON is conceptually scalable to large, heterogeneous datasets, its validation in this study focuses on controlled underwater inspection data collected for pipeline applications. ROVON enables the representation and analysis of multimodal underwater data by semantically annotating raw sensor feeds, enforcing data integrity, and leveraging knowledge graphs to convert disparate inputs into actionable insights. The ontology demonstrates how a structured semantic approach facilitates advanced analysis that improves decision-making, supports proactive maintenance strategies, and enhances operational safety. The proposed framework was validated through a controlled pipeline inspection scenario.Engineering Technolog
How Hurricane Harvey Impacted Thornwood
No full textIn 2017, Hurricane Harvey devastated a large part of Houston, Texas. While the flooding lasted less than two weeks, the physical impact of the hurricane remains present. It was clear to see the flooded houses, molded wood, and the destruction of trees and other personal property. Even greater than physical destruction, there was a massive financial, social, and political toll that overtook most Houstonians, especially those directly impacted. My research explores how Thornwood, a small neighborhood in the energy corridor in Houston that was one of the most impacted by the hurricane, experienced the hurricane and, importantly, the recovery. The neighborhood was made up of mostly retired middle class older couples who had lived there for years and newly formed families in their first house. This project is comprised of interviews of people who were in the area and is synthesized with the community members’ memoirs, an ethnography of the neighborhood, and an autobiography. My research is presented in a written format including some figures within the document. This research can provide a better understanding of how much hurricanes impact lives but specifically how Thornwood was affected.Business Administratio
Animal Assisted Therapy and Healthcare Workers: A Systematic Review [paper]
No full textIntroduction: Healthcare workers (HCW) in modern society are plagued with emotional exhaustion, declining rates of job satisfaction, and increasing levels of anxiety. The crisis HCWs face, driven by increasing stressors, shapes the prevalence of burnout. This systematic review evaluates current research on Animal-Assisted Therapy (AAT) as an intervention for HCW. Methods: Guided by Jean Watson’s Theory of Human Caring, the review synthesizes research from seven peer-reviewed studies conducted between 2020 and 2025 throughout the United States. Research employs numerous techniques, including qualitative, quasi-experimental, and observational designs, and incorporates opinions and descriptors from 785 participants across all seven studies. Results: Studies showcase positive correlations between reduced anxiety, improved mood, and preferred method of treatment for HCWs. Research limitations throughout this research discuss the small sample sizes, geographic concentration, and reliance on self-reported data. Discussion: The overall message of the findings of this research for HCWs describes AAT as an initiative to promote well-being and retention of HCWs. AAT as an intervention is already changing the landscape of patient care, and when HCWs are afforded this therapeutic technique, a cultural transformation will occur.Nursin
Oral history interview: Clarence Wolivier
No full textEdited and unedited transcript files (.pdf) and edited and unedited video files available with closed captioning.Oral history interview with Katherine Selber about family friend, Clancy Wolivier
Intermittent Fasting and Culturally Conscious Dietary Guidance in Hispanic Immigrants: A Systematic Review [paper]
No full textNo abstract prepared.Nursin
Controlled Dry Adhesion of Bio-Inspired Fibrillar Polymers: Mechanics, Strategies, and Recent Advances
No full textRecent advancements in tunable adhesion technologies have broadened the scope of applications for bio-inspired fibrillar adhesives. This review highlights the latest developments in controlled adhesion mechanisms, with a focus on bio-inspired fibrillar systems. We examine key theoretical foundations and progress in controllable adhesion, including contact mechanics, contact splitting efficiency, fracture mechanics, and the interplay between adhesion and friction. Various factors influencing adhesion strength are discussed alongside optimization approaches and innovative designs that enhance performance. The review also covers recent research on switchable adhesion strategies, with an emphasis on methods for regulating surface contact, stress distribution, and shear force control. Finally, we identify the primary challenges and future directions in the field, outlining areas that require further exploration and technological development. This paper aims to provide a comprehensive overview of current advancements and offer insights to guide future research in the evolving field of tunable adhesion technologies.EngineeringEngineering Technolog
Oral history interview: Dalton Southern Jr.
No full textEdited and unedited transcript files (.pdf) and edited and unedited video files available with closed captioning.Oral history interview with Michelle Crittenden about her uncle, Dalton Southern Jr
Fingerprint-Driven Predictive Modeling for Efficient Job Scheduling in High Performance Computing Systems
No full textModern high-performance computing (HPC) systems must manage increasingly diverse workloads running on heterogeneous architectures. Yet, conventional schedulers such as SLURM and PBS continue to operate under the assumption that all jobs exhibit similar behavior, disregarding application-specific resource demands. This mismatch leads to inefficient resource allocation, suboptimal system utilization, and increased job wait times. This thesis introduces a novel methodology for creating unique signatures of jobs submitted to HPC centers such that these "fingerprints" can be taken into account when job scheduling decisions are made. Our approach leverages historical telemetry data to construct interpretable job fingerprints-- behavioral signatures that capture actual resource usage patterns. By clustering jobs with similar fingerprints, the scheduler can match incoming jobs to the most suitable resources, including accounting for power constraints. We develop a complete pipeline that encompasses interpretable fingerprint generation, classification, and resource usage prediction, culminating in a multi-objective scoring mechanism to guide scheduling decisions. Experimental evaluation on two real-world HPC datasets shows that our method significantly improves CPU utilization, reduces power consumption, lowers job wait time and turnaround times compared to traditional heuristics. This work demonstrates how predictive analytics and job fingerprinting can overcome key limitations of current schedulers, offering an explainable, data-driven approach for managing heterogeneous HPC workloads.Computer Scienc