Open Research Oklahoma (Oklahoma State Univ.)
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Impact of student teaching on the self-efficacy and importance of instructional practices in SBAE pre-service teachers
No full textSchool-based agricultural education (SBAE) is experiencing a discrepancy between the demand for new SBAE teacher hires and the supply of graduates from teacher preparation programs (AAAE, 2024). Most new SBAE teacher hires are traditionally certified graduates of university teacher preparation programs (AAAE, 2024). Their preservice teacher training concludes with a clinical student teaching internship as the capstone experience (Kasperbauer & Roberts, 2007b; Paulsen et al., 2015). The purpose of this study was to determine the instructional practice needs of student teachers at the beginning and end of their student teaching experience. Bandura’s (1977) self-efficacy theory provided the theoretical framework for the study, which addresses a person’s self-efficacy to complete a specific task successfully. The study implemented a casual-comparative design as data were collected at two different points during the training of preservice teachers: before and after student teaching. Fourteen universities in NAAE Region II provided the names and school email addresses of student teachers for the spring 2024 semester. Thirty-five student teachers at eight universities in five states provided complete responses for Phase I and II data collection. Data were collected using a modified instrument from an Oklahoma SBAE teacher needs assessment (Branscum et al., 2023). Participants were asked to rate 26 select instructional practice areas on a five-point Likert-type scale for perceived self-efficacy and importance. Wilcoxon Matched-Pairs Signed Ranked Tests were conducted to compare the change in self-efficacy and perceived importance of instructional practices after student teaching. Instructional practice self-efficacy increased significantly (p < 0.05) in 21 areas after student teaching. Developing lesson plans decreased significantly in perceived importance after student teaching. The Ranked Discrepancy Model (RDM) was administered to rank the instructional practice needs by calculating the positive, negative, and tied ranks (Narine & Harine, 2021). Instructional areas with the greatest perceived need for additional training were Modifying instruction for students with special needs, Motivating students to learn, and Managing student behavior. Instructional practice need rankings were similar for preservice teachers before and after student teaching. The findings of the study guided 22 conclusions along with implications and recommendations for practice and research
Foaling management and care of the nursing foal
No full textThe Oklahoma Cooperative Extension Service periodically issues revisions to its publications. The most current edition is made available. For access to an earlier edition, if available for this title, please contact the Oklahoma State University Library Archives by email at [email protected] or by phone at 405-744-6311
Review of COVID-19 vaccine use and adverse effects in U.S. commercial airline pilots
No full textTo airline passengers, the health of their pilot is primary. During the coronavirus (CV) pandemic, pilots across the U.S., either willingly or due to corporate and/or government mandates, took the EUA vaccines (Pfizer, Moderna, and J&J). The normally vigorous drug approval process for pilots was abandoned, in an effort to get passengers back flying and save the industry. The safety systems that protect these pilots and the National Airspace System—the Federal Aviation Administration, their employers, and their unions—disengaged leaving these pilots with few choices: be vaccinated, be terminated or navigate the onerous exemption process. Although choice advocacy and grass-roots organizations sprung up across the industry pushing back against the mandates, it is estimated, similar to the general population, at least 80% of commercial pilots are vaccinated, which the CDC defines as having at least one dose of any of the three EUA approved CV-19 vaccines. This research examines the adverse effects of those vaccines in the U.S. pilot population. Through an anonymous, online, cross-sectional survey, civilian pilots from all major airlines, many charter, fractional, and corporate operators, and military pilots across the services disclosed their medical maladies, vaccine details, personal narratives, and opinions about oversight, safety, and the future. 1622 pilots responded which was further reduced to an 1132 airline pilot only sample size. Over 23% of respondents report suffering adverse effects from the vaccines. Yet, few are willing to disclose this information for fear of losing their careers. Pericarditis and myocarditis proved to be a statistically significant problem among this population (p=.0158739, SI.05). However, little, if any, research has been conducted on this population. Congressional oversight is one of the many recommendations that resulted from this research as 78% of airline pilot participants stated that they believe “safety risks exist due to the CV-19 vaccines.
Buckling analysis and design of varying stiffness lattice-cored sandwich panels
No full textLocal panel buckling often dominates lightweight aircraft wingbox design. To enhance buckling performance, this thesis maximizes the buckling load of composite sandwich panels with various aluminum lattice cores and tow-steered graphite-reinforced polymer composite facesheets. The lightweight lattice core increases bending stiffness, while tow-steered laminated composite facesheets redistribute stress to improve buckling performance. Effective material properties are determined using a beam theory based homogenization approach, and the 3D lattice core is modeled as a 2D equivalent plate in MSC Nastran. Various lattice geometries (square, triangular, hexagonal) are explored. Optimization under stress constraints reveals that straight composite fibers with an optimized angle in facesheets result in a square core panel achieving about 46 times increase in the buckling load and a 32% weight reduction compared to aluminum counterpart. Triangular and hexagonal core panels show about 14 and 20 times increases, respectively, with significant weight reductions. Curvilinear fiber patterns(linear and non-linear) further enhance buckling load without increasing weight, with linear fibers providing about 18% increase and non-linear fibers about 36% increase in buckling load for square core panel compared to straight fibers. Triangular and hexagonal core panels also show significant improvements in buckling load with curvilinear fibers. Notably, square aluminum lattice core with aluminum facesheets have the lowest buckling load, while tow-steered composite laminates with non-linearly varying fiber paths achieve the highest buckling load increment. Experimental and computational bending analyses show that triangular lattice-cored resin-based sandwich panel withstand the highest bending load, approximately 16% and 8% higher than square and hexagonal panels, respectively. Despite this, the triangular panel is the heaviest. The square panel has the highest load-to-weight ratio, approximately 22% greater than the triangular and 24% greater than the hexagonal panel.
To optimize lattice cores for sandwich panels, the asymptotic homogenization (AH) approach is used for its shape independence, though it requires complex and costly fine meshes. Thus, this study introduces an image processing method that converts unit cell images into binary matrices, eliminating the need for traditional mesh generation. A Convolutional Neural Network (CNN) developed using square unit cell images and AH-derived effective material properties demonstrated high accuracy with minimal error during training, offering the potential for cost-effective property predictions based solely on images. A parametric study highlights the need for sufficient training data, as accuracy drops significantly when data is reduced below half of the original
Adult clothing demonstration: Second year demonstration no. 1; Renovation of clothing
No full textThe Oklahoma Cooperative Extension Service periodically issues revisions to its publications. The most current edition is made available. For access to an earlier edition, if available for this title, please contact the Oklahoma State University Library Archives by email at [email protected] or by phone at 405-744-6311
Agricultural faculty undergraduate advisors’ perceptions of well-being
No full textThe COVID-19 pandemic inspired researchers to study well-being of students and faculty at higher education institutions. Few studies, however, focused on well-being of faculty undergraduate advisors (FUAs). Well-being is cited as a root cause for an exodus of higher education professionals. These professionals, including FUAs, are important to student retention. In colleges of agriculture (COA), FUAs can provide a connection to the agriculture and natural resources industry that students from non-agriculture backgrounds may often lack; but little is known about their level of well-being regarding this component of their faculty role. This study aimed to determine the well-being of FUAs at Oklahoma State University according to the Positive Emotions, Engagement, Relationships, Meaning, and Accomplishments (PERMA) model. The levels of each PERMA construct were tested to identify differences in self-perceived well-being between the comprehensive role of a faculty member and their role as FUAs in OSU. A statistically significant difference was found between faculty’s self-perception of well-being as a faculty member and as a FUA (P: t = 5.76, p < 0.001; R: t = 5.16, p<0.001, M: t = 3.89, p < 0.001; A: t = 3.99, p < 0.001). FUA well-being averaged between 2.46 and 2.90 on a scale of 1 to 5 (P = 2.46, R = 2.90, M = 2.51, A = 2.62), indicating a low level of well-being related to the role. Could the low level of self-perceived well-being by faculty members regarding their role as a FUA impact their job satisfaction and retention? We recommend COAs consider the influence of FUA well-being upon student-advisor relationships, and, consequently, student retention. We recommend COAs develop advising models that benefit faculty and students, such as dual or supplementary models, allowing faculty to maintain vital relationships with students while also maintaining well-being in their comprehensive faculty role
Overview of food insecurity and food bank usage before and after the COVID-19 pandemic in Eastern Oklahoma
No full textFood insecurity remains a major problem in Oklahoma and across the entire United States. The COVID-19 pandemic created burdens on households and individuals that may have contributed to increased food insecurity. However, there is still limited research surrounding COVID-19’s effect on food security within the United States as a whole and Oklahoma. This project aims to increase the knowledge base surrounding COVID-19 and food insecurity. Data from 67 food banks in Eastern Oklahoma was analyzed using paired t-tests to identify how visits for households, children, adults, seniors, and individuals changed during and after the pandemic’s peak transmission time. The paired t-tests only showed a significant increase in senior visits within the examined time frame of 2016 to 2023. The results of this analysis reveal that the food insecurity landscape in Eastern Oklahoma during and after the COVID-19 pandemic is very complex, and seniors may have been more affected by the COVID-19 pandemic than entire households, children, or adults
Wind field estimation using machine learning based reduced-order models for safe wind-aware navigation of unmanned aerial systems in urban spaces
No full textReduced Order Models (ROMs) may be defined as techniques to simplify high-fidelity and complex physical systems to a lower-complexity model. They can capture and reproduce the inherent behavior of the system using either the data generated by the system or simplified approximation for its governing equations. ROMs are thus extensively used to reduce complex simulations’ computational costs and are a crucial enabler for informed decision-making. Successful implementation of ROMs to represent the flow dynamics accurately and their related applications is an active research area of great interest. With the advent of Machine Learning (ML) and Deep Learning techniques, ROMs can now be devised using Artificial Neural Networks. Employing specialized neural networks like Recurrent Neural Networks (RNN), Long-Short-Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN), underlying physics and patterns in the data could be embedded to generate Machine-Learning based Reduced Order Models.
Wind field estimation is essential in developing and testing Unmanned Aerial Systems (UAS) and related sub-systems. Deploying the UAS is vital since path-planning and real-time pilot awareness are necessary for safety during operation, especially in urban spaces. To enable wind field estimation with low computational cost, we generate Reduced Order Models to assist UAS operations. For generating the data to devise the ROMs, we rely on well-established large-eddy simulation model solvers like the Parallelized Large Eddy Simulation Model (PALM), Open-Source Field Operation and Manipulation (Open FOAM), and Cloud Model 1 (CM1).
Firstly, we propose different ways to generate machine learning-based ROMs. Various numerical experiments are conducted for atmospheric and simplistic urban flows to demonstrate their effectiveness. In the second part, we explore the impact of reduced-order wind on fixed-wing aircraft’s flight dynamics commonly used for Advanced Air Mobility (AAM) operations. Findings and comparisons demonstrate that fixed-wing aircraft with different wing-loading respond differently to the reduced order and full order wind fields obtained from the Large Eddy Simulations. This work aims to lay the groundwork for wind-aware navigation of UAS in urban spaces by predicting wind fields using ML-based ROMs
Choosing words wisely: Influences on literature selection in Oklahoma classrooms
No full textThe study explores the challenges teachers in Oklahoma's secondary schools face when incorporating diverse perspectives into their classroom literature. There is a disconnect between state standards that push for inclusivity and the pressures from the local community, political climate, and resource shortages. Through interviews, two teachers explain how they work through these tensions, aiming to diversify the curriculum while also managing the expectations of more conservative viewpoints. Despite the challenges, the teachers’ efforts align with Bishop’s (1990) “Mirrors, Windows, and Sliding Glass Doors,” aiming to provide students with self-reflection and new perspectives. The research highlights the need for more support and further exploration into thematic teaching and community collaboration to help overcome these barriers and foster inclusive, critical-thinking classrooms
Effects of integrated management for invasive sericea lespedeza (Lespedeza cuneata) control in the tallgrass prairie: Fire, herbicide, grazing, and the role of germination timing and shade on spread and survival
No full textInvasive species are a global threat, serving as the second leading cause of reduced biodiversity and costing over 1 billion dollars annually. In the tallgrass prairie, one invasive species is especially damaging: sericea lespedeza (Lespedeza cuneata). While others have explored the use of fire and herbicide to control sericea, large-scale, replicated experiments using regionally standard management techniques like whole pasture burning and grazing are lacking. Moreover, additional research is needed to assess how shading and freezing temperatures affect sericea seedlings and explain some potential mechanisms for effective control methods. We conducted a large-scale field study and two greenhouse experiments to assess how fire, herbicide, shade, and seedling age at first frost affect sericea spread and survival. Specifically, we burned six large, grazed pastures (> 350 ha) and treated portions of each with fall applications of metsulfuron-methyl to assess how burn season, timing of herbicide application, and seasonal grazing by yearling stocker steers affect sericea cover and seed production in the Flint Hills of Oklahoma. Additionally, we designed two greenhouse experiments to test the effect of seedling age at first frost on over-winter survival and the effect of shade on the growth and survival of sericea seedlings. Sericea cover varied with burn season, with sericea cover increasing or staying the same after a dormant season burn but decreasing or staying the same after a growing season burn. We observed the greatest sericea control with a late growing season fire (August) followed by a metsulfuron-methyl application 13.5 months after the burn, which resulted in a 30% reduction in upper canopy sericea cover (< 1% cover post-treatment) and reduced seed production (64 seeds/stem). Sericea cover and seed production were lower in the sprayed treatments than in the unsprayed controls, regardless of burn season. Likewise, over-winter survival and above and below-ground biomass were lower for sericea seedlings germinating in September than those germinating in June. Shade also negatively impacted sericea seedling growth, with shaded seedlings exhibiting lower above and below-ground biomass than unshaded seedlings. These findings highlight the potential role of growing season fire, herbicide applications, and shading for sericea control