College Degree Attainment in the Mountain West, 2010-2020

This fact sheet presents data on the gaps in college degree attainment in the Mountain West states of Arizona, Colorado, Nevada, New Mexico, and Utah. The data appear in “Learning and Earning by Degrees: Gains in College Degree Attainment Have Enriched the Nation and Every State, but Racial and Gender Inequality Persists,” report published by the Georgetown University Center on Education and the Workforce (CEW), a research and policy institute within Georgetown University that focuses on the connections between education, career qualifications, and workforce demands

Election Performance in the Mountain West, 2008-2020

This fact sheet presents data on the Election Performance Index (EPI) and voter turnout in the Mountain West states of Arizona, Colorado, Nevada, New Mexico, and Utah for the 2008, 2012, 2016, and 2020 presidential elections, as originally reported by the Massachusetts Institute of Technology Election Data & Science Lab in their Elections Performance Index

Advancements in Solar Energy Life Cycle Assessment and Implications for Power Plants in the Western US

New solar energy technologies, such as second-generation photovoltaic panels, are increasingly being installed at utility scale. Our accounting of the life cycle climate change impacts or carbon footprint of solar power technologies needs to be revised as technologies change and as prior omissions of related impacts are identified. This thesis aims to update the life cycle climate change impacts of two solar energy technologies. First, the direct land use change (DLUC) and albedo change impacts from removing native vegetation and installing CSP troughs were incorporated into life cycle assessment (LCA) scenarios for the four currently operational CSP trough power plants in the Southwest US. Through geospatial analysis, the original aboveground and belowground biomass carbon, organic soil carbon, and net primary productivity loss, and the before-and-after albedo difference of these sites were determined and converted to their contributions to the life cycle climate change impacts of CSP. The carbon-related impacts of the environmental change from desert soil to human infrastructure showed only a minor contribution (\u3c 5%) to the overall life cycle climate change impacts of electricity generated by CSP trough facilities. Albedo change impacts of three of the facilities, Mojave, Solana, and Genesis, show a similarly minimal contribution; however, the albedo change impacts of the Nevada Solar One CSP power plant result in a 11.27% increase in the total life cycle climate change impacts. In the second study, a cradle-to-grave LCA was performed for a 200 MW power plant comprised of cadmium telluride (CdTe) solar panels by Nevada Gold Mines, using primary data from the company. The study revealed a carbon footprint of 10.17 g CO2eq/kWh, a notable reduction from the LCA results of previous versions of CdTe panels. The carbon payback time of the facility was determined to be only 6 months in relation to the greenhouse gas emissions intensity of grid electricity. These new LCA results for CSP trough and CdTe panels can assist in building more accurate greenhouse gas inventories and in planning for an energy transition that minimizes greenhouse gas emissions

Homelessness Among School-Aged Children in the Mountain West, 2022-2023

This fact sheet presents data on homelessness among school-aged children in the Mountain West during the 2022-2023 school year. The data are sourced from the “Child and Youth Homelessness in the United States: Data Profiles” dashboard produced by SchoolHouse Connection and Poverty Solutions at the University of Michigan. State-level data profiles include information about child and youth homelessness at the national, state, county, school district, and congressional district levels. This fact sheet focuses on homelessness among school-aged children within the Mountain West states of Arizona, Colorado, Nevada, New Mexico, and Utah

Relationship Closeness and Eating Disorder Pathology in Undergraduate Female Friends Dyads

Eating disorders (ED) are one of the leading mental health concerns among undergraduate students Rates nearly doubled from 15% in 2013 to 28% in 2020 and 2021 Relationship closeness may play a crucial role in both development and recovery from EDs. Limited research on platonic relationships and ED pathology, specifically relationship closeness.https://oasis.library.unlv.edu/durep_posters/1264/thumbnail.jp

The Role of Chronobiology in Sleep, Physical Activity, and Stress Responses: Implications for Health and Performance

Introduction: Chronobiology, the study of internal biological rhythms, provides essential insights into how synchronization with these rhythms influences physiological processes, including sleep, metabolism, inflammation, and cognitive function. The modern lifestyle often disrupts these natural rhythms, resulting in circadian misalignment, which has been linked to impaired health outcomes such as increased inflammation, oxidative stress, and metabolic dysregulation. While much attention has been given to chronotype, the individual preference for activity timing, and its impacts on health and performance, less is known about how chronotypical alignment influences physiological and psychological responses in specific occupational contexts or everyday life. This dissertation aims to address these gaps by comprehensively examining the role of chronobiology in modulating physiological and behavioral responses to sleep disruption, physical activity, and occupational stressors, with particular attention to wildland firefighting.Methods: Three interconnected studies were conducted to explore the impact of chronotype, social jetlag (misalignment between biological and social clocks), and exercise timing on inflammatory and oxidative stress biomarkers, sleep quality, physical activity, and mental health outcomes. In the first study, the effects of acute sleep deprivation were investigated on inflammatory and oxidative stress responses during simulated wildland firefighting scenarios in male participants. Biomarkers were assessed at baseline, immediately post-exercise, and during recovery. The second study monitored wildland firefighters over a two-week critical training period, analyzing how chronotype and social jetlag influenced inflammatory biomarkers, body composition, and recovery patterns. Chronotype and social jetlag were assessed using validated questionnaires, while physiological biomarkers were measured using blood samples collected pre- and post-training. The final study investigated the effects of aligning habitual exercise timing with chronotype in a general adult population. Physical activity levels, sleep quality, and mental health parameters were evaluated using questionnaires and statistical analyses to compare chronotypically aligned versus misaligned exercise groups. Results: The studies collectively demonstrated that circadian alignment affects physiological and psychological responses to stressors and exercise. Specifically, the acute sleep deprivation study revealed heightened inflammatory and oxidative stress responses during simulated wildland firefighting, exacerbated by sleep loss. In wildland firefighters, misalignment between chronotype and training schedules impacted inflammatory biomarkers, suggesting greater susceptibility to stress-induced physiological disruptions among those experiencing higher social jetlag. Additionally, the third study showed that chronotype-aligned exercise notably improved mental health outcomes compared to those with misaligned exercise schedules. Conclusions: This dissertation provides compelling evidence that aligning daily activities, especially physical exercise, with individual circadian preferences can optimize health, enhance performance, and improve overall quality of life. These findings highlight the critical importance of considering chronotype and circadian rhythms in occupational health and general public health interventions. Future research should further explore chronic interventions and their potential benefits in mitigating the negative effects of circadian misalignment and sleep disruption on health and performance outcomes

MFGAT: Map-Free Trajectory Prediction with Graph Attention Networks for Autonomous Vehicles

Accurate trajectory prediction is a key component for ensuring safe and efficient navigation of autonomous vehicles in complex traffic scenarios. While traditional methods rely heavily on high-definition (HD) maps, these approaches face significant challenges, including high costs, limited availability, and susceptibility to rapid obsolescence. This thesis proposes an end-to-end, map-free trajectory prediction model that leverages Graph Attention Networks (GAT) to dynamically capture spatial-temporal interactions among road agents, eliminating the need for HD maps.The research introduces UNLVTraj, a novel LiDAR-based dataset collected around the University of Nevada, Las Vegas campus, specifically along Cottage Grove Street, Harmon Avenue, and Maryland Parkway. This dataset captures diverse traffic scenarios with annotated trajectories, addressing a gap in existing resources by providing realistic, campus-specific interactions for validation. The proposed model combines GAT with Temporal Convolutional Networks (TCN) and a sequence-to-sequence framework, enabling adaptive weighting of agent interactions to enhance prediction accuracy. Experimental results demonstrate the model’s effectiveness across different environments. On the ApolloScape benchmark, the model achieves a 5.98% reduction in Average Displacement Error (ADE) and a 6.76% reduction in Final Displacement Error (FDE) compared to baseline method. Evaluations on the UNLVTraj dataset further validate its robustness in predicting trajectories for heterogeneous agents, even in unstructured, map-free settings. By offering a scalable, cost-effective solution and a publicly available dataset, this work supports future research in map-free navigation

AI in Medical Practice and Education: A Work in Progress

At the University of Nevada, Reno School of Medicine, we’ve been working for the past two years to integrate AI into pre-clinical medical education in ways that prepare students for the tools and challenges of future AI-augmented clinical practice. This includes formal curricular workshop exposure and guided experimentation with AI in data-driven capstone projects. In addition, ongoing projects include the pilot of a research design and workflow tool and the integration of ambient documentation into our simulation center. This session will reflect on what we’ve done, what we’re building toward, and what we’ve learned along the way, shaped by ever evolving technology and the constraints of limited resources

Assessment Of Vegetation Dynamics After South Sugar Loaf and Snowstorm Wildfires Using Remote Sensing Spectral Indices

Wildfires are increasingly common in sagebrush ecosystems across the western United States, leading to vegetation loss and ecosystem restructuring. This study investigated vegetation recovery within two large Nevada burn scars, the Snowstorm Fire of 2017 and the South Sugar Loaf Fire of 2018. Landsat 8 surface reflectance imagery supplied multi temporal spectral data, and vegetation burn severity was mapped with the difference Normalized Burn Ratio. The research aimed to quantify how vegetation health spectral indicators respond over time across severity gradients and to detect shifts in land cover composition from pre fire to post fire conditions.Four spectral indices—NDVI, MSI, MCARI2, and land surface temperature—were assessed with the Mann Kendall trend test, followed by a linear mixed effects model that linked time and severity class to spectral change. ISODATA clustering was conducted to determine pre-fire and post-fire classes, with a stability matrix and change matrix tracking the fate of each pre-fire class with post-fire classification. LANDFIRE EVT maps were assessed to test their sensitivity to short term ecosystem change. Severity mapping showed contrasting fire behavior. Snowstorm burned chiefly at low severity and covered sixty nine percent of the area, whereas South Sugar Loaf contained similar proportions of high and moderate high severity at thirty-two and thirty three percent respectively. NDVI rose at both sites, confirming recovery. In Snowstorm the increase was uniform across severity classes, but in South Sugar Loaf low severity pixels gained more greenness than high severity ones, indicating that high severity had an impact on vegetation regrowth. MSI declined over time at both sites, indicating moisture recovery, with the largest decline in moderate high severity zones at South Sugar Loaf, indicating the role of burn severity, MCARI2 a chlorophyll proxy, climbed steadily and was again most responsive in the lower severity classes of South Sugar Loaf as compared to high severity, whereas there was no effect of severity in Snowstorm fire. Land surface temperature declined at Snowstorm fire area and showed localized reductions in low severity areas at South Sugar Loaf. The Mann-Kendall trend test was non-significant for fire areas but showed localized positive trends. Linear Mixed Effects model and Mann-Kendall test revealed time since fire to be the primary driver of recovery, while severity modulated local trajectories. ISODATA distinguished five pre-fire and seven post-fire classes at South Sugar Loaf and five pre-fire and post-fire classes at snowstorm. South Sugar Loaf underwent pronounced compositional change: its dominant shrubland class retained just one third of its original area, shifting largely to herbaceous and sparse vegetation categories. Conifers in South Sugarloaf fire lost most of it area to sparse vegetation in post-fire. Snowstorm exhibited greater stability for shrubs species, with more than half of the Herbs Shrubs Mix-2 class persisting. Across both fires new post fire clusters displayed lower near infrared and higher short wave infrared reflectance, signatures typical of ash and bare substrate. LANDFIRE EVT maps failed to register these changes, remaining static in both South Sugar Loaf and Snowstorm. Combining time series spectral metrics with unsupervised classification captured both gradual recovery and abrupt compositional shifts, offering a comprehensive view of postfire dynamics. The persistence of outdated classes in LANDFIRE EVT underscores the need for more agile vegetation mapping frameworks that can accommodate rapid ecological change in fire prone landscapes

Elevating Education: Leveling Up Individual Learning Plans

This three-article dissertation investigated the effectiveness, implementation quality, and automation of Individual Learning Plans (ILPs) in promoting college and career readiness. Article 1 analyzed High School Longitudinal Study of 2009 data and found that ILPs did not significantly guide course alignment. Article 2 examined ILP implementation across Nevada high schools, revealing inconsistent quality, limited standardization, and few culturally responsive practices. These findings informed the creation of a new high-quality ILP framework. Article 3 employed a convergent parallel mixed methods design to assess an automated ILP prototype based on this framework. Participants in the automated group reported significantly higher scores in engagement, user experience, and cultural relevance. Thematic and sentiment analyses supported these results, with participants expressing greater confidence, clarity, and motivation. This dissertation expanded the ILP literature by identifying key gaps, creating a new framework, and introducing automation as a scalable, equitable solution for improving implementation