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Ethnoracial Characterization of Cognitive Function and Activities of Daily Living in Neurodegenerative Diseases
No full textObjective: Neurodegenerative diseases, such as Alzheimer\u27s disease (AD), lead to progressive cognitive and functional decline, which significantly affects individuals’ ability to perform their everyday Activities of Daily Living (ADLs). Occupational Therapist (OTs) assess cognitive impairments and their impact on daily function by utilizing tools like the Montreal Cognitive Assessment (MoCA) to guide intervention planning. However, research on the MoCA’s effectiveness across ethnoracial groups is limited, raising potential biases in the accuracy of cognitive assessments. This study examines the relationship between cognitive function, measured by the MoCA, and functional impairment, assessed using the Functional Activities Questionnaire (FAQ), among ethnoracial older adults with neurodegenerative diseases. This research aims to investigate potential disparities in those with differing levels of cognition and the overall effectiveness of cognitive screening tools. Methods: A stratified random sample was identified to analyze data from 600 participants drawn from the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS). Participants included community-dwelling older adults aged 55 and above, representing three ethnoracial groups: Non-Hispanic White, Hispanic White, and Black Non-Hispanic. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA), while functional abilities were measured through the Functional Assessment Questionnaire (FAQ). Statistical analyses included Pearson Correlation Coefficient to examine the relationship between cognitive and functional performance and one-way ANOVA to identify potential differences in MoCA and FAQ scores across ethnoracial groups. Robust methods were applied to account for statistical assumption violations, ensuring accurate results interpretation. Results: Analyses included 600 participants divided into three ethnoracial groups: Non-Hispanic White (n = 200), Hispanic White (n = 200), and Black Non-Hispanic (n = 200). A one-way ANOVA revealed no statistically significant differences in MoCA scores among the groups, F(2, 597) = 2.11, p = .123. However, Levene’s test indicated unequal variances (p \u3c .001), and robust tests (Welch’s and Brown-Forsythe) confirmed the non-significant findings. For FAQ scores, a significant group difference was observed, F(2, 597) = 9.15, p \u3c .001, and robust tests supported this result (Welch’s F = 10.92; Brown-Forsythe F = 9.15, both p \u3c .001). NHW participants had the highest mean FAQ score, indicating greater functional impairment. A Pearson correlation revealed a strong negative relationship between MoCA and FAQ scores in the overall sample (r = -0.623, p \u3c .001), with similarly significant negative correlations within each group. Fisher’s r-to-z transformation indicated that the strength of the correlation between cognitive and functional scores differed significantly between NHW and both Black Non-Hispanic (z = 2.156, p \u3c .05) and Hispanic White participants. Conclusion: The results indicate that MoCA total scores are largely consistent across Non-Hispanic White (NHW), Hispanic White, and Black Non-Hispanic older adults, with no significant differences in the overall analysis. However, Fisher’s r-to-z transformation revealed significant differences in the relationship between cognitive performance and functional abilities when comparing NHW participants to Hispanic White and Black Non-Hispanic participants. These findings suggest that the MoCA remains a reliable tool for cognitive screening across ethnoracial populations. Further research is needed to explore additional factors, including cultural and contextual influences, that may shape cognitive performance and ratings of functional outcome
Investigating Decision Factors for Modularization and Standardization Decision on Early Project Phase: Qualitative Comparative Analysis
No full textModularization involves shifting site-based work to offsite locations to enhance overall productivity, reduce costs, shorten schedules, and improve project competitiveness. By developing and utilizing consistent designs, facility standardization further optimizes project schedules, costs, and value. When combined, they create a leveraging opportunity, as seen in the shipbuilding and manufacturing industries. While both strategies offer significant advantages, capital projects struggle to have a well-informed modularization and standardization decision during the early project phase (i.e., Opportunity Framing). This often results in improper implementation and lower modularization and standardization levels. The main cause for this is the industry’s limited understanding of early-phase high-level decision factors to justify and implement modularization and standardization decisions. To address this, this research investigates modularization and standardization decision factors along with their combinatorial and interactive impacts on modularization and standardization decisions using Qualitative Comparative Analysis (QCA). The study conducts a detailed analysis of the decision factors using descriptive analysis and compares the results with the QCA findings to have a comprehensive understanding of decision factors as well as respective modularization and standardization decisions. To do this, the research has adopted the twelve modularization decision factors identified by Construction Industry Institute (CII) Research Team 396 and ten standardization factors adopted by CII RT UMM-01, analyzing real-world project data to assess their implications on decisions. The findings will assist industry practitioners in making informed decisions on modularization and standardization, strengthening justification, and improving modularization and standardization implementation. Subsequently, the results and the findings of the research will enhance the existing body of knowledge on early modularization and standardization decisions. The research aids in providing a better understanding of decision factors and their combinatorial and interactive effects on modularization and standardization decisions and provides recommendations for informed decisions in capital projects
The Dropbot: Design and Development of a Custom Drone for Precision Water Drop Penetration Time (WDPT) Testing
No full textAssessing the hydrophobic characteristics of soil is vital for understanding soil wettability or soil-water interactions, particularly in post-wildfire environments where water repellency can significantly impact ecosystem recovery, water infiltration, and erosion control. One key metric in soil wettability studies is the Water Drop Penetration Time (WDPT) test, which evaluates the hydrophobicity of soil and guides land treatment strategies. This thesis presents the design and development of DropBot, a custom-built drone platform engineered for the precise delivery and analysis of water droplets in WDPT tests.The DropBot, a custom drone, integrates a lightweight, 3D-printed frame with a self-leveling platform, enabling consistent droplet deployment and testing across varied terrains. The system is equipped with modular manipulators, a custom droplet release mechanism, and multiple onboard sensors for environmental feedback. The DropBot is operated using the Robot Operating System (ROS) framework, allowing for scalable sensor integration, data acquisition, and autonomous behavior. The development process included iterative mechanical design, sensor calibration, and field testing to ensure precision, stability, and repeatability. Experimental results demonstrate the DropBot’s ability to accurately deliver water droplets with controlled positioning and timing, validating its utility for in-situ WDPT tests. This work also contributes to the growing field of agricultural robotics and sets the stage for further enhancements, such as computer vision integration for surface detection and fully autonomous mission planning for soil property mapping
More Than a Feeling: A Mixed-Methods Examination of Transgender Community Connectedness and Associations with Suicidality
No full textTransgender adults experience significantly higher rates of suicidal ideation than the general population. A sense of transgender community connectedness (STCC) may reduce this risk, but predictors of STCC and its association with suicidal ideation are understudied. This cross-sectional mixed methods study examined STCC and past-year suicidal ideation in relation to dimensions of transgender community connectedness (TCC) at each level of the Social Ecological Model using data from a U.S. survey of transgender adults (n = 126). About half (52.4%) felt connected to the transgender community, and 27.0% reported past-year suicidal ideation. Hierarchical logistic regression models for both outcomes showed good fit. In the STCC model, visibility lost significance after the first step, while sufficient trans ties predicted 2.96 times higher odds of STCC (aOR = 2.96, 95% CI [1.12, 7.84], p = .029), compared to those with insufficient trans ties. Relational factors, including high perceived peer support (aOR = 5.92, 95% CI [1.88, 18.59], p = .002) and received peer support (aOR = 2.71, 95% CI [1.01, 7.24], p = .039), were also significantly associated with higher odds of STCC compared to low support. In the suicidal ideation model, odds were 69% lower for those with a college degree compared to those without, and 77% lower for those with high STCC compared to low STCC. Odds were 7.47 times higher for those reporting serious psychological distress and 4.33 times higher for those who received peer support, compared to those without distress or who did not receive support. Open-ended responses emphasized TCC’s role in fostering safety, self-expression, existential validation, lifesaving support, and belonging. Findings support the need to develop a multilevel, multidimensional TCC measure, research the mechanisms by which TCC infl uences suicidality, and identify intervenable correlates of suicidality to inform community-based interventions
Nevada HPV Vaccine Completion Time and Adherence Among 9-14-Year-Olds From 2017-2023
No full textThe HPV (Human Papillomavirus) is one of the most common sexually transmitted infections, in the United States. Since its development in 1991, the HPV vaccine has been proven to be highly effective against the infection (Centers for Disease Control and Prevention [CDC], 2024). In order for an individual to receive optimal protection, the CDC has given guidelines dependent on age. It is important to understand changes in HPV vaccine completion for future public health immunization campaigns and objectives. To analyze how HPV completion and adherence has changed from 2017-2023 among the 9-14-year-old population, a retrospective cohort study was conducted utilizing secondary data from Nevada’s Immunization Information System (NV WebIZ). Vaccination records (n= 108,070) were used to determine the prevalence of completion in Nevada. Across the years 2017-2023, there was a low prevalence of HPV completion across adolescents. A nonparametric analysis was used to determine differences in the amount of time it took to receive the second dose of the HPV vaccine over time and across different demographic groups. There was no statistically significant trend over the years (p= 0.44). There were statistically significant differences in completion time among demographic groups including age, gender, racial background, and insurance types (p \u3c 0.001). A logistic regression was conducted to understand how demographic factors predict vaccination adherence. Gender, county, race, year of initiation, initial insurance, and initial provider were all significant covariates of the regression model (p\u3c 0.05). Despite the statistical significance, there were limited practical differences among groups. This study provides an overview of HPV completion across adolescents and can be used to understand HPV vaccination in Nevada
Process over Product: Rethinking Assignments in the age of AI
No full textHow do we design assignments that invite students to engage deeply with ideas rather than simply complete and submit finished products? Featuring faculty from across the University of Nevada, Reno alongside facilitators from Advancements in Teaching Excellence, this panel explores assignment design across disciplines, focusing on strategies that foreground thinking as a learning process. Panelists from across disciplines will discuss how they are rethinking assignments to emphasize process over product, using an AI-aware approach to build guardrails on the ways in which students use (or do not use) these tools while also designing assignments and activities to engage students in thinking that leads to deeper learning. Rather than framing AI as a threat to academic integrity, this session highlights ways in which we can intentionally design assignments to emphasize critical thinking, metacognition, and process-based assessment to support authentic learning.
Dr. Barker will explain how fourth-year French students, enrolled in an Open Educational Resources (OER) course, integrate AI into the course’s primary scaffolded writing project. She will outline the design of the project, clarify each step of the process, and share examples from student work to illustrate both opportunities and challenges. By highlighting the ways AI supports language learning, critical engagement, and creative expression, Dr. Barker aims to provide concrete strategies for incorporating AI into classroom practice. Attendees will gain insights into balancing innovation with pedagogy, while also considering the ethical and practical implications of student use of AI in higher education.
Dr. Munro will explore “ungrading” as a practice that emphasizes learning processes over final products, an approach particularly relevant in the age of AI. As AI tools make it easier to generate polished assignments, ungrading shifts the focus from outputs to authentic engagement, reflection, and growth. Through self-assessment, and iterative feedback, students learn to evaluate their own progress and embrace risk-taking. This approach fosters resilience, critical thinking, and adaptability while addressing AI-related concerns by valuing process and learning over easily automated products.
Using examples from his MBA courses, Dr. Perera will demonstrate how AI can be integrated into assignments not only to generate solutions, but also to refine and extend them. Drawing on cases in forecasting and quality analytics, he will illustrate how students leverage AI for faster problem-solving and for producing rich, informative visualizations through effective prompt engineering. These insights underscore a critical lesson: while AI can serve as a “smart intern” to replicate and validate business solutions, a strong foundation in the underlying concepts remains equally essential. By blending domain knowledge with AI-driven tools, Dr. Perera will highlight how students can achieve both efficiency and deeper understanding, preparing them for a future where AI is not a substitute for knowledge, but a partner in decision-making and learning
Rural County International Immigrant Population Growth in Nevada, 2020-2024
No full textThis fact sheet presents 2020-2024 data on international immigrant population growth across 17 Nevada counties.The Daily Yonder report, “International Immigrants Drive Rural Population Growth,” uses U.S. Census data to highlight international immigration patterns for all metropolitan and nonmetropolitan (rural) counties across the United States. This fact sheet highlights data on 13 nonmetropolitan counties in Nevada: Churchill, Douglas, Elko, Esmeralda, Eureka, Humboldt, Lander, Lincoln, Lyon, Mineral, Nye, Pershing, White Pine. This fact sheet also includes data on four metropolitan counties in Nevada: Carson City, Clark, Storey, and Washoe
Reflections Through AI: Visualizing Learning, Emotion, and Mindset
No full textAttendees will learn about narrative identity, using AI as a reflective tool, and create a reflective artifact using AI. Narrative identities are stories that can help students make sense of their prior learning experiences. Negative experiences in math classrooms can influence students’ perceptions of the subject and sometimes cause negative feelings or anxiety towards learning math. For years, I have used drawing in conjunction with written explanation to observe my students’ thoughts and emotions towards learning mathematics. In my most recent iteration of using this activity in an undergraduate math class, one student described their feelings of learning math to Chat GPT and Chat GPT produced an image. This image connected the student to their unconscious image of math. The student will describe how AI helped shift their emotions towards math to a more positive mindset. Ideas will be shared for activities to showcase how AI can serve as a medium for students to visualize and process their emotional connections to learning, discuss how AI can be used as a tool for reflection, and, more broadly, how this use of AI can improve the teaching and learning culture
Extremely Low-Income Renter Households in the Mountain West, 2023
No full textThis fact sheet examines data from the 2025 National Low Income Housing Coalition (NLIHC) report, “The Gap: A Shortage of Affordable Rental Homes,” which reported annual rental housing shortages in the United States. This fact sheet focuses on the number of extremely low-income renter households, affordable and available rental homes, and associated cost burdens in the five Mountain West states of Arizona, Colorado, Nevada, New Mexico, and Utah
Reading the Rainbow: Exploring Themes and Identities in LGBTQIA+ Picture Books
No full textThis presentation shares an analysis of over 275 picture books featuring LGBTQIA+ identities, providing evidence that the depiction of queerness in children’s literature is complex and varied. Tools for defining narrative themes and LGBTQIA+ identities will be shared as well as data on queer characters with intersecting identities. By exploring the nuances of LGBTQIA+ representation in picture books, attendees will gain a better understanding of novel research inquiries into representation