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Examining Social Capital and First-Generation Student Status: Evidence from a Midwest University
No full textAs demand for college graduates grows (Times Higher Education, 2023) persistence rates for first-generation students continue to lag (Toutkoushain et al., 2018, 2021). Complicating our understanding of this opportunity gap is the diversity of how first-generation student status is defined (Jo Peralta & Klonowski, 2017). We argue that while notable differences exist between first-generation students whose parents have no college experience (FGS-none), and those from families with collegiate backgrounds (continuing-generation), the distinctions between first-generation students with parents who have some college experience (FGS-some), and the other two groups are unclear. As such, this study investigates the implications of varying definitions of first-generation college student status through the lens of student social capital and retention. Using a comprehensive survey and data analysis, the study reveals no significant differences in students’ cumulative social capital. However, examining social capital through principal component analysis revealed disparities in family social capital (FGS-none and FGS-some) and peer social capital (FGS-none). Further, lower levels of cumulative social capital and family social capital were significantly related to lower retention. The findings underscore the importance of academic support tailored to first-generation students, particularly FGS-none, and incorporating strategies within curriculum aimed at bolstering the social capital of first-generation students, especially during their critical freshman year. We argue that while post-secondary institutions should continue to use a broad definition of first-generation student status to catch the most at-risk students, research should continue to collect and explore the nuances between first-generation student populations and the role of social capital in student success
A Historical Review of Course Requirements in Agricultural Mechanics for Agricultural Education, Teacher Education Undergraduates at Nine 1862 Land-Grant Universities
No full textPreparing pre-service students to teach agricultural mechanics is a difficult task due to the career pathway including a great variety of topics (Byrd et al., 2015). This study examined the trend in requirements in 1862 land-grant universities for number of courses, course credit hours, and course topics related to agricultural mechanics, as well as the total credit hours needed for degree completion in agricultural education, teacher education. Overall, the universities examined had lessened the total course credit hours related to agricultural mechanics courses from 8.33 to 11.83 credit hour requirements in 1980, to a range of 6.67 to 8.33 credit hours in 2021. As such, the total credit hours required for teacher education degree completion in agricultural education had decreased from an average of 128.4 total credit hours to 125.0 credit hours over the same time period. Course topics found to be the most common from 1980 to 2021 were Intro to Ag Mechanics, Welding, and Construction/Structures. It is recommended that qualitative interviews be conducted with cognizant university faculty to ascertain the importance of the various course topics and analyze how topics were identified as priorities in their respective programs, including whether the views of industry stakeholders were considered and addressed
Programmatic Accreditation: Implementation Strategies for College Kinesiology Program Directors
No full textProgrammatic accreditation within Kinesiology sub-disciplines, such as physical therapy and sports medicine, have typically been required at the graduate level. Within the coming decade, major certifications for exercise physiology (ACSM-EP) and strength & conditioning (CSCS) will institute programmatic accreditation requirements at the undergraduate level. This article takes a narrative review approach at examining ways that programmatic accreditation at the undergraduate level may impact incoming undergraduate students, such as necessitating earlier specialization and limiting the ability for late-stage career changes. To mitigate these impacts, college kinesiology program directors may benefit from considering the perspectives of students as they work to meet programmatic accreditation requirements. This includes considering how curriculum and program design, communication to incoming students, and the potential for inter-institutional collaborations may impact students’ ability to make informed decisions and reach their career goals. College program directors may find utility in candid conversations with prospective students on the potential certifications they could earn after graduation, curriculum design that allows for some flexibility in a student’s academic journey, and working with neighboring institutions to refer students to programs that match their career goals
Surviving and Thriving on the Gold-Capped Mountain; the Entrepreneurial Sucessess of Chinese Women in Early Nevada
No full textAs a result of shifts in historical methodologies, women’s history began to rise in prominence but only recently have historians shifted their focus to specific marginalized groups. Chinese women in Nevada at the turn of the 20th century are noteworthy due to their entrepreneurial successes in a time of rampant discrimination, yet have not been discussed in the literature as a collective. By analyzing the impacts of anti-Chinese legislation, public commentary in newspapers and political cartoons, and first-hand accounts of Chinese women from the region during the period this project develops an untold narrative. Secondary material provides further claims on the existence and significance of the female Chinese population in early Nevada. This project is based on the works of Sue Fawn Chung about the ethnic group in Nevada and in the West. Results show that the way in which Chinese women in Nevada occupied certain labor niches was due largely in part to the unique backdrop of the state’s policies towards aliens and the nature of Nevada’s economy. While most Chinese women are reduced to their roles as working girls in Nevada mining towns, there is evidence that they were actually successful, hard-working women despite the overwhelming historiography and primary source material that claims the contrary. These findings suggest that by studying the multiple facets of Chinese women\u27s opportunities during this period there is value in identifying the gendered differences experienced by an immigrant group, labor being one of many lenses of which to investigate such important distinctions.https://oasis.library.unlv.edu/durep_podium/1054/thumbnail.jp
Comparing Forgetting Rates Between Item and Relational Memories
No full textForgetting is an everyday occurrence where an idea that could be recalled successfully is no longer able to be retrieved (Tulving, 1974). Multiple theories suggest how forgetting occurs, such as the decay theory, which suggests that memories are gradually forgotten over time, and the interference theory, in which forgetting occurs because of competing information. Additionally, there are two newer theories which both predict that item representations should be forgotten because of interference and mnemonic discrimination should be forgotten due to decay: the memory system-dependent forgetting hypothesis (Hardt, Nader, & Nadel, 2013), and the representation theory of forgetting (Sadeh et al., 2014). The present study examined these theories by comparing the rate of forgetting for mnemonic discrimination and item recognition over five days. Using the Mnemonic Similarity Task (MST) (Stark & Kirwan, 2019), participants completed memory tasks over a five-day period, allowing for an assessment of both decay and interference effects. Results show that mnemonic discrimination is more prone to decay, while item recognition is more prone to interference-based forgetting. These results supported the present hypothesis, supporting both the memory-system dependent forgetting hypothesis and the representation theory of forgetting. Furthermore, these findings supported Wickelgren’s (1975) model, a quantitative model of forgetting that has been overlooked for decades and predicts that interference and decay contribute to forgetting independently. Overall, this study contributes to the growing understanding of the distinct mechanisms underlying memory decay and interference in the forgetting process.https://oasis.library.unlv.edu/durep_posters/1214/thumbnail.jp
The Utilization of String Pulling to Determine Female Rats\u27 Dual Cognitive Motor Task Performance Following Space Flight Stressor Exposure
No full textDuring deep space exploration, astronauts depend on mission-essential performance in Dual Cognitive Motor Tasks (DCMT), such as receiving information from ground control to assemble and configure wires during a spacewalk. DCMT relies upon processing cognitive and motor information simultaneously. Previous research on rodent models has shown various impairments in cognition and motor function in separate tasks after exposure to deep space radiation and sleep fragmentation (SF). However, an assessment of DCMT performance in rodents has not been established. Rats and humans use similar hand-over-hand movements to pull a string, and task demands may be varied to assess cognition and motor function simultaneously using this behavior. Therefore, string-pulling behavior was used to develop DCMT to assess performance in female outbred Wistar, retired breeder, rats exposed to 10 centi-grays of 250 MeV/n Helium (n=9) and sham (n=8) with pre-/post-SF conditions. During DCMT, rats had to discriminate between string pairings that varied in cues and order presentations. Rats were given up to 30 trials/day to fulfill criterion (four consecutive correct string pairing selections) before undergoing one 12h session of SF in a chamber containing a bar that swept horizontally every two minutes. Results thus far show that rats engaged in increased test and trial quantities following SF regardless of irradiation status. Additional analyses are ongoing, and the results will be delivered at the poster presentation. DCMT in SF irradiated rats may provide translational insights into how spaceflight stressors impact mission-critical performance.https://oasis.library.unlv.edu/durep_posters/1216/thumbnail.jp
Detection of Methane Leaks by the Use of Mid-range Infrared Camera
No full textTimely detection of methane leaks from natural gas infrastructure, like pipelines and valves, is essential for mitigating environmental and safety risks. This research focuses on using mid-wave infrared (MwIR) cameras on unmanned aerial systems (UAS) to detect leaks. By leveraging machine learning, the study aims to develop an efficient, real-time methane inspection system that operates directly on embedded processors on UAS platforms.
This project employs the FLIR G300a OGI camera for remote gas inspection, utilizing video preprocessing and optical flow to mask gas plumes in footage. The YOLOv8 deep learning model is used to detect gas pixels and segment the plume area, with the analytics deployed onboard an NVIDIA Jetson Nano.
Indoor experiments demonstrated that the FLIR G300a camera can effectively detect low methane flow rates (3-5 SCFH) at a 30 ft distance, highlighting its strong performance under controlled conditions. In outdoor settings, detection proved more challenging due to factors like wind, temperature, and complex backgrounds. However, preprocessing videos and applying optical flow significantly improved gas pixel identification, enhancing labeling and training for the YOLOv8 model, and demonstrating the system’s adaptability to real-world environments.
The developed methodology automates the previously labor-intensive and hazardous task of detecting methane leaks with handheld sensors, which require close proximity to the leak source. This remote, automated approach enhances efficiency, enabling fast response and timely mitigation of environmental, safety, and property risks. Additionally, the technology can be adapted to detect other gasses around chemical and industrial facilities.https://oasis.library.unlv.edu/durep_posters/1239/thumbnail.jp
Application of Machine Learning Algorithms in Healthcare
No full textMachine Learning (ML) is a subset of artificial intelligence that has made substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few fields of healthcare. Here we provide a brief overview of machine learning-based approaches and learning algorithms. Second, we discuss a general procedure of ML and review some studies presented in ML application for several healthcare fields. We also briefly discuss the risks and challenges of ML application to healthcare.This dissertation also consists of four different cases in healthcare where we have applied ML techniques on real life data sets. In the first case study, Random Forest (RF) method has been used with high accuracy for classifying a rare skin disease Erythemato-squamous Dermatosis. In the second case study, Logistic regression analysis was utilized in finding the risk factors associated with Alcoholic hepatitis (AH). A sub-analysis was performed to determine variables associated with mortality in AH patients. In the third case study, Linear Discriminant Analysis (LDA) & RF were utilized for classifying five types of cancer (breast cancer, kidney cancer, colon cancer, lung cancer and prostate cancer) based on high dimensional microarray gene expression data. Principal component analysis (PCA) was used for dimensionality reduction, and principal component scores of the raw data for classification. In the fourth case study, we aim to discover the potential factors behind the initiation and then possibly sustain the desire to quit smoking using the LDA & RF method
Mining Gambling Data for Modeling Gambling Behavior Patterns
No full textUnderstanding player behavior for responsible gambling research is a difficult task due to the lack of data on players’ activities. Past studies in this area are largely limited to publicly available behavioral data or aggregated players data. Problem gambling in gamblers is typically identified only after they have already been addicted or have already been engaging in problematic gambling behavior. Furthermore, “risky” gambling behavior has historically been difficult to define due to the varying patterns of gambling activity that could potentially be attributed to it.In this dissertation we illustrate the methodology and algorithms used to engineer financial data for further analysis. We demonstrate the use of time-series analysis and anomaly detection using statistical and unsupervised machine learning methods to detect anomalous individual player behavior. This is accomplished using a custom metric (delta index) for statistical analysis and the spectral residual algorithm for machine learning based anomaly detection. We also demonstrate the use of longitudinal clustering using Gaussian Mixture Models to identify changes in player behavior at 30 day time intervals. This method groups players based on changes in behavior over time rather than their existing behavior in any given time slice. Additionally, we demonstrate the use of this model to track individual player behavior over time. The behavior analysis methods illustrated in this dissertation can be generalized for accepting additional behavioral features for further research in this area. Additionally, these methods can be further adapted for use in other behavioral studies
α-QUARTZ Plastic Strength Investigation Via Diffraction Experiments on Novaculite Using a D-Dia and Elastic Plastic Self-Consistent Interpretation
No full textThe plastic response of experimentally deformed quartz provides insight into the strength of ductile shear zones that has implications for tectonic, lithospheric, and ore deposit models. We present a suite of 15 uniaxial deformation experiments on Arkansas novaculite conducted in a DDIA apparatus with in-situ synchrotron x-rays. Experimental temperatures range from 25 C to 1334 C with pressures between 1.39 GPa and 3.1 GPa, and strain rates between 1x10-5 s-1 and 9x10-6 s-1. Macroscopic sample strain ranges from 3% to 24%. d-spacings from the (101), (110), (200), (201), and (112) lattice planes were measured, producing lattice strain up to ~5%. Diffraction data was forward modeled using elastic plastic self-consistent (EPSC) simulations to derive differential stress and the critical resolved shear stress (CRSS) of individual slip systems as a function of pressure and temperature. This study investigates the activation of basal (c), positive rhombohedral {r}, negative rhombohedral {z}, prismatic {m}, positive acute rhombohedral {pi}, negative acute rhombohedral {pi’} slip systems and their association with differential strain among quartz lattice planes during deformation prior to steady state flow. Modeled stress strain curves reflect a decrease in critical resolved shear stress with increased temperature and decreased stress. Microstructures observed from electron backscatter diffraction (EBSD) maps of our deformed samples indicate the presence of dauphine twins whose effects may be reflected in the alignment of the positive rhombohedral plane (10-10) in our deformation experiments. Additionally, inelastic behavior at low strain observed in all deformation experiments has been simulated with the use of an isotropic deformation system in the model. The differential stresses derived from EPSC simulation of our D-DIA experiments have been used to compare with deformation data from Griggs type experiments, that correlate well