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Urban Services District Lighting Study
Get PDFThe Louisville-Jefferson County Metro Government addressed areas within the right-of-way that had poor lighting levels and might contribute to vehicle crashes or crime occurrence. GIS data from the state and local government was used to identify hot spots where additional lighting might be desired. These hot spots were evaluated, and additional lighting locations were recommended for implementation
PSO Scoring Instruction Guide
Get PDFThis document is a scoring guide to assist higher-education administrators, faculty, and researchers who wish to use the Professional Skills Opportunities instrument (PSO). There are four aspects, or factors, that the PSO is intended to measure relative to engineering undergraduate students’ opportunities to practice professional skills and an overall PSO score. Detailed scoring instructions are provided. The PSO was developed to assess students’ opportunities to develop and practice a range of professional skills. Utilizing a rigorous instrument development process, the PSO was shown to be a tool that can reliably and validly be used to assess engineering undergraduate students\u27 professional practice preparation
The Role of Trait and Specific Expectations in the Experience of Dysmenorrhea
No full textDysmenorrhea is the most common pain condition in reproductive-aged women and is characterized by pain during menstruation. Dysmenorrhea has far-reaching effects, such as increased psychological distress, increased relationship problems, reduced physical activity, and decreased sleep efficiency. It is the leading cause of absenteeism in this population. Though dysmenorrhea impacts many women and has such far-reaching effects, it has not been studied as extensively as other pain conditions, specifically regarding trait expectations and specific expectations. In other pain conditions, hope and optimism, the two most studied trait expectations, are protective factors for pain severity, interference, and the psychological effects of pain. Trait expectations additionally predict specific expectations, with hope predicting specific expectations related to the self, and optimism predicting specific expectations related to experiences. Both self- and experience-expectations predict outcomes, such as pain severity and pain tolerance. The current study sought to 1) examine the factor structure of specific expectations for dysmenorrhea; 2) examine the roles of trait and specific expectations in predicting dysmenorrhea; and 3) examine the roles of trait expectations and dysmenorrhea in predicting psychological adjustment. A longitudinal study of 389 menstruating women, over the age of 18, who could read and write English was conducted through CloudResearch. Confirmatory factor analysis and mixed latent- and measured-variable path analysis were used. Results indicated two distinct factors of specific expectations, pain-expectation and selfexpectation. Hope predicted self-expectation, which predicted dysmenorrhea interference. Painexpectation predicted dysmenorrhea severity and interference. Additionally, both hope and optimism predicted psychological adjustment. Dysmenorrhea interference predicted psychological distress. However, trait expectations did not predict dysmenorrhea. This study is the first to examine the associations among trait expectations, specific expectations, and dysmenorrhea and expands on the differences between dysmenorrhea and other pain conditions
Harmonizing STEM and Social Justice Mathematics: A Path to Global Equity and Empowerment
Get PDFThis study delves into the interconnectedness of science, technology, engineering, and mathematics (STEM) and social justice mathematics (SJM) to address pressing equitable global sustainable development and social justice issues. The research aims to understand how educators can effectively use SJM in STEM teaching to empower students as change agents for global equity by aligning mathematical tasks with the United Nations\u27 2030 agenda for Sustainable Development Goals (UN, 2015) and using them to address real-world injustices. The study unveils educators\u27 pedagogical strategies for creating supportive learning environments and nurturing students\u27 emotional well-being in STEM subjects. Educators who are conscious of social justice must consider how their affective views relate to SJM and STEM pedagogical strategies and are more likely to be empathetic to their students’ learning
Accidental Outreach and Happenstance Staffing: A Cross-Institutional Study of Writing Center Support of First-Generation College Students
Get PDFFirst-generation students (FGS) make up a significant percentage of college populations. However, they experience hardships that are less common for their continuing-generation peers. They struggle to understand the “rules” of college and lack the cultural capital that can help students succeed through generations of knowledge about how to navigate college. Writing centers attempt to lessen these burdens by providing outreach to marginalized student populations, including FGS. However, there has been a lack of cross-institutional research that examines exactly how writing centers support FGS. This article presents a mixed-methods study that begins to close that knowledge gap and demonstrate common patterns of FGS support across institution types in the United States. Results show that most FGS support is “accidental” and highly context-specific, which makes measuring success difficult. The results of this study also show that tutor staffing and training play a significant role in FGS support and should be further researched in writing center studies. The author argues that we need to do more assessment of our outreach and its outcomes for FGS, going beyond our narratives of what does or does not work for marginalized students
On Linguistic Justice in the Writing Center: A Genesis Story
Get PDFThis essay presents a reflective account of the origin story of a linguistics justice initiative within the writing center. It concludes by posing questions aimed at promoting dialogue among writing center practitioners as they consider similar initiatives within their own contexts
AI-Enhanced Decision-Making for Course Modality Preferences in Higher Engineering Education during the Post-COVID-19 Era
Get PDFThe onset of the COVID-19 pandemic has compelled a swift transformation in higher-education methodologies, particularly in the domain of course modality. This study highlights the potential for artificial intelligence and machine learning to improve decision-making in advanced engineering education. We focus on the potential for large existing datasets to align institutional decisions with student and faculty preferences in the face of rapid changes in instructional approaches prompted by the COVID-19 pandemic. To ascertain the preferences of students and instructors regarding class modalities across various courses, we utilized the Cognitive Process-Embedded Systems and e-learning conceptual framework. This framework effectively delineates the task execution process within the scope of technology-enhanced learning environments for both students and instructors. This study was conducted in seven Iranian universities and their STEM departments, examining their preferences for different learning styles. After analyzing the variables by different feature selection methods, we used three ML methods—decision trees, support vector machines, and random forest—for comparative analysis. The results demonstrated the high performance of the RF model in predicting curriculum style preferences, making it a powerful decision-making tool in the evolving post-COVID-19 educational landscape. This study not only demonstrates the effectiveness of ML in predicting educational preferences but also contributes to understanding the role of self-regulated learning in educational policy and decision-making in higher education
Material Characterization and Determination of MEPDG Input Parameters for Indiana Superpave 5 Asphalt Mixtures
Get PDFSuperpave 5 (SP 5) has the ability to slow asphalt binder aging in asphalt pavements, which is why the SP 5 mix with optimum asphalt binder content to yield 5% air voids has recently been used in Indiana roads. INDOT also uses the AASHTOWare Pavement ME design software in pavement design, and the current asphalt aging prediction model in Pavement ME was developed based on the conventional Superpave asphalt mixture (design air voids 4%) design method. For the successful use of the SP 5 mixture design method with Pavement ME, the input level and input parameters play a significant role. The objective of this study was to determine pavement performance using the three different input levels (Level 1, 2, and 3) and to recommend the necessary Pavement ME input parameters for SP 5 mixtures for accurate pavement performance prediction. The results show that Levels 2 and 3 are underpredicting or overpredicting the pavements’ distresses. Therefore, to capture the benefit of SP 5 pavement design, the Level 1 inputs (lab test results) were recommended for the Pavement ME. The findings of this research will provide guidance on using accurate input parameters for the Pavement ME design for SP 5 mixtures, resulting in more accurate asphalt pavement performance predictions during the pavement design process. It is anticipated that this will result in longer asphalt pavement service lives, which is a cost-effective benefit for INDOT
Granular Estimation of User Cognitive Workload Using Multi-Modal Physiological Sensors
Get PDFMental workload (MWL) is a crucial area of study due to its significant influence on task performance and potential for significant operator error. However, measuring MWL presents challenges, as it is a multi-dimensional construct. Previous research on MWL models has focused on differentiating between two to three levels. Nonetheless, tasks can vary widely in their complexity, and little is known about how subtle variations in task difficulty influence workload indicators. To address this, we conducted an experiment inducing MWL in up to 5 levels, hypothesizing that our multi-modal metrics would be able to distinguish between each MWL stage. We measured the induced workload using task performance, subjective assessment, and physiological metrics. Our simulated task was designed to induce diverse MWL degrees, including five different math and three different verbal tiers. Our findings indicate that all investigated metrics successfully differentiated between various MWL levels induced by different tiers of math problems. Notably, performance metrics emerged as the most effective assessment, being the only metric capable of distinguishing all the levels. Some limitations were observed in the granularity of subjective and physiological metrics. Specifically, the subjective overall mental workload couldn\u27t distinguish lower levels of workload, while all physiological metrics could detect a shift from lower to higher levels, but did not distinguish between workload tiers at the higher or lower ends of the scale (e.g., between the easy and the easy-medium tiers). Despite these limitations, each pair of levels was effectively differentiated by one or more metrics. This suggests a promising avenue for future research, exploring the integration or combination of multiple metrics. The findings suggest that subtle differences in workload levels may be distinguishable using combinations of subjective and physiological metrics
A Review of Computational Fluid Dynamics Approaches Used to Investigate Lubrication of Rolling Element Bearings
Get PDFOptimizing bearing performance is based on effective lubrication, especially in high-speed machinery, where minimizing churning and drag losses is of significant importance. Over the past few decades, extensive research has been conducted into the better understanding of different aspects of bearing lubrication. These investigations have employed a combination of experimental methods and advanced computational fluid dynamics (CFD) models. This article provides a comprehensive overview of critical aspects of bearing lubrication, with a specific emphasis on recent advances in CFD models. Lubricant flow and distribution patterns are discussed while examining their impact on drag and churning losses. An extensive discussion is provided on the meshing strategies and modeling approaches used to simulate various flow phenomena within bearings. In addition, relevant trends and impacts of cage design on bearing lubrication and fluid friction have been explored, along with a discussion of prevailing limitations that can be addressed in future bearing CFD models