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Practical Intelligence: Generative AI Toolkit for Nurse Education
No full textThis toolkit delivers a structured introduction to generative AI tailored for nurse educators, offering practical guidance across six domains: foundational concepts; course and lesson design; assignment development; scholarly applications; simulation integration; and strategies for facilitating student engagement with AI. Beginning with an accessible overview of how generative models function and where they fail. It presents frameworks for critical evaluation and transparent use. Subsequent sections demonstrate how to leverage AI for organizing curricula, writing objectives, crafting assessments, and generating discussion prompts, all aligned with professional standards. Applications in scholarship cover literature search planning, peer‑review simulation, and evidence‑based question development. Simulation chapters guide co‑design of scenarios and pilot testing. Finally, the toolkit addresses ethical, legal, and pedagogical considerations for student use, ensuring educators maintain academic rigor and clinical accuracy
Individual Differences Matter: Examining the Relationship Between Transfer Students’ Identity and Social Presence in Online Learning
No full textThis study investigates the relationship between transfer students’ perceptions of identity and their social presence within online learning environments, addressing a gap in research on this underexplored population. Drawing on Erikson\u27s (1968) psychosocial development theory and Marcia\u27s (1966) identity status model, the study explores how identity formation influences both the perception and manifestation of social presence in the online learning environment. A sequential explanatory design (Creswell & Poth, 2025) was employed, combining quantitative data from a web-based survey on identity status and social presence perceptions, followed by content analysis of students\u27 discussion posts to examine social presence indicators. The findings emphasize the critical role of recognizing transfer students\u27 identity status in the design of online learning environments. By incorporating social presence, educators can foster greater engagement and support academic success for a uniquely diverse student population
Desert Slavery: How the Old Spanish Trail Sustained Captivity and Coerced Labor in the North American West
No full text“Desert Slavery: How the Old Spanish Trail Sustained Captivity and Coerced Labor in the North American West,” examines the Old Spanish Trail as a central corridor of enslavement in the North American West. Captive-taking and bondage regularly occurred along the Old Spanish Trail, which connected northern New Mexico to southern California. Indeed, the movement of enslaved people through the Great Basin helped maintain the Old Spanish Trail. The seventeenth and eighteenth centuries witnessed the development of coerced labor and enslavement as Spanish Mexican and Indigenous cultures intersected to create a system of slavery unique to New Mexico. However, as the Spanish empire expanded further north and west in the late eighteenth century, and then as Mexico achieved independence in the early nineteenth century, Euro-American traders, trappers, and settlers who entered the region adopted and spread these practices. The nineteenth century saw the introduction of Black chattel slavery to the area and the overlapping presence of these two systems of bondage along the trail. By the 1860s and 1870s, when the United States claimed the region, all forms of bondage were being curtailed, which helped bring an end to the use of the Old Spanish Trail. To the extent that previous historians have addressed bondage along the trail, they have focused on the Indigenous slave trade without highlighting the presence of Black chattel slavery. Yet the history of the trail is not complete without consideration of both systems of enslavement. This dissertation bridges the gap between the historiography of Black chattel slavery and Indigenous slavery along the Old Spanish Trail and the region it traversed
Difference in Condylar Position, Dimension, and Angle Among Different Vertical Skeletal Patterns, A Retrospective CBCT Study
No full textIntroduction: This study aimed to evaluate and compare the condylar dimensions, position, and angulation among individuals with different vertical skeletal patterns—hyperdivergent, hypodivergent, and normodivergent—using cone-beam computed tomography (CBCT). The objective was to determine whether vertical skeletal variations influence temporomandibular joint (TMJ) structure and function, providing insights for improved orthodontic diagnosis andtreatment planning.
Methods: A retrospective analysis was conducted on CBCT scans from 185 adult patients (370 TMJs) from the orthodontic clinic at the University of Nevada, Las Vegas. Patients were categorized into three vertical skeletal groups based on the Frankfort horizontal-mandibular plane angle (FMA): hypodivergent (\u3c 20°), normodivergent (20°–30°), and hyperdivergent (\u3e30°). Condylar width, length, height, joint spaces, and condylar angulation were measured using OnDemand 3D software. Descriptive statistics, ANOVA, and post hoc analyses were performed to evaluate differences across skeletal patterns, sex, and ethnicity.
Results: Significant differences in condylar morphology and position were found among the three skeletal groups. Hyperdivergent individuals exhibited significantly smaller medio-lateral condylar widths (p \u3c 0.05) and reduced superior joint spaces, indicating a higher condylar position within the TMJ. Males had significantly larger condylar dimensions than females, with wider medio-lateral condylar widths and greater superior and posterior joint spaces (p \u3c 0.05). Ethnic differences were also observed; African American participants had significantly greater condylar height, while Caucasian individuals had the largest medio-lateral condylar width compared to Hispanics.
Conclusions: This study highlights the influence of vertical skeletal pattern, sex, and ethnicity on condylar morphology and spatial positioning. Hyperdivergent individuals demonstrated distinct condylar adaptations, which may have clinical implications for TMJ assessment and orthodontic treatment planning. Understanding these variations is essential for personalized orthodontic and orthopedic interventions
Forecasting Hotel Demand Using Stacked Generalization
No full textDemand forecasting is one of the most critical functions of hotel revenue management. It serves as the bedrock for strategic and operational decision-making across all facets of a hospitality organization, including pricing, inventory management, staffing, risk management, and purchasing. In fact, the success of a hotel is largely predicated on its ability to accurately and consistently predict and adapt to future trends. Now, with the proliferation of artificial intelligence and advances in information technology, demand forecasting in hotels stands to benefit significantly. Yet, the literature has only recently started to explore these solutions. Accordingly, there is a lack of research that rigorously develops and applies cutting-edge machine learning methodologies. In this dissertation, I fill this gap by rigorously validating and evaluating the accuracy and interpretability of machine learning models as well as introducing the concept of meta-learning in the field hotel revenue management using hotel occupancy data from a large casino hotel. The models were evaluated over various forecasting horizons and accuracy metrics, resulting in a series of experiments that showed that model performance varied across forecasting horizon and between model types. In doing so, I found that the suggested meta-model had the highest performance for further-out time horizons, but that the traditional models yielded better performances at the shorter time horizons. These results suggest that forecasting is not a one-size-fits-all exercise, but rather a search for the best forecasting method for the particular market, forecasting horizon, or dataset. This phenomenon is particularly important for both academics and practitioners, as it implies that models must be built with methodological rigor and contextual awareness in order to achieve the most accurate results
Empowering Science with the World\u27s First High Accuracy and High Throughput Functional Assay
No full textUnderstanding the functional consequences of genetic mutations remains a central challenge in modern biology, with far-reaching implications for human health and disease. While early systematic methods like alanine scanning and phage display provided foundational insights into protein structure and function, the emergence of high-throughput approaches—such as Multiplexed Assays of Variant Effect (MAVEs)—and predictive tools powered by artificial intelligence have vastly expanded our ability to profile mutational landscapes. However, these methods are often constrained by trade-offs between accuracy, scalability, and biological relevance.This dissertation presents the development and application of the GigaAssay, the world’s first high-throughput functional assay capable of delivering both high accuracy and scalability. Built upon a modular, one-pot experimental framework, the GigaAssay enables the quantitative assessment of thousands of mutations simultaneously, while maintaining single-molecule resolution through the use of hundreds of unique molecular identifier (UMI) barcodes per variant. The technology\u27s generalizability and robustness are demonstrated by exploring the mutation space of two vastly different proteins, HIV-1 Tat and HER2. In addition to detailing the experimental and computational innovations that underpin the GigaAssay, this work highlights its transformative applications in virology and oncology, offering new avenues for functional genomics, drug development, and precision medicine. By enabling systematic and reproducible functional interrogation of genetic variation at unprecedented scale and accuracy, the GigaAssay empowers a new era of biological discovery
A Comprehensive Study on The Use of Sub-Coherent Self Heterodyne Linewidth Estimations
No full textA fundamental requirement for many precision spectroscopic applications is the development of stable lasers with narrow linewidths. However, accurately verifying laser linewidths becomes increasingly challenging as the linewidth decreases. In the field of frequency metrology, the delayed self-heterodyne technique has long been regarded as one of the most reliable and accessible methods for linewidth characterization, favored for its low equipment cost and reproducible results. Traditionally, self-heterodyne measurements employ an optical delay line significantly longer than the coherence length of the laser, with analysis performed via either the instantaneous power spectral density or the noise power spectral density. However, the use of long optical fibers introduces several limitations: substantial optical attenuation requiring amplification stages, and susceptibility to thermally induced Brownian noise, which can artificially broaden the measured spectrum. This work utilizes a short delay fiber – less than the laser’s coherence length can yield reliable and accurate linewidth measurements. The primary objective of this thesis is to provide a comprehensive methodology for extracting the laser linewidth from the power spectral density of a sub-coherence delay self-heterodyne signal, thereby offering an improved framework for precision laser characterization
Integrated UAV Platform for Multi-Spectral, Thermal, and EOS Imaging in Wildfire Monitoring and Modeling
No full textThis thesis presents the design and development of a modular unmanned aerial vehicle (UAV) system based on a quadcopter platform for flexible and efficient wildfire-related multimodal image acquisition. Addressing key limitations in ecological UAV monitoring such as sensor inflexibility, time-consuming reconfiguration, and imprecise image georeferencing, the system introduces a versatile payload integration framework supporting three distinct imaging sensors: MicaSense Altum-PT, FLIR Vue Pro R, and Sony Alpha 6000.All onboard components, including the flight controller, autopilot software, GNSS module, motors and ESCs, were selected to optimize stability and payload performance. A gimbal-free, downward-facing mount simplifies field deployment, while custom integration enables precise geotagging. Field tests were conducted over grassland environment using mission-based corridor scans. The resulting imagery was processed in PIX4Dmapper to generate dense point clouds, orthomosaics, digital surface models (DSMs), and vegetation indices. A comprehensive evaluation of flight performance and data quality was performed to validate the system’s effectiveness. The proposed platform enhances UAV-based ecological monitoring in pre- and post-wildfire scenarios by supporting rapid sensor swapping and robust data collection. It facilitates high-resolution analysis of vegetation, soil, and burn impact, contributing to improved wildfire assessment and post-fire ecosystem recovery monitoring. This research contributes to the National Science Foundation (NSF) EPSCoR project “Harnessing the Data Revolution for Fire Science (HDRFS)” through the Cyberinfrastructure Innovations (CII) component
Exploring the Processing of Objects, Settings, and Global Properties in Natural Auditory Scenes
No full textOur auditory system has a remarkable ability to make sense of complex environments, allowing us to identify what we are hearing and where sounds come from. This process of auditory scene analysis is essential for situational awareness, decision-making, and effective communication in daily life. While much is known about how listeners segregate and identify individual sound sources (e.g., voices), less is understood about how global properties of a scene (e.g., openness, naturalness) contribute to perception. This dissertation examined how object- and setting-level information in natural auditory scenes are processed, and whether they rely on distinct or overlapping mechanisms. In Experiment 1, participants listened to 200 scenes of varying durations (1, 2, or 4 sec) and listed the setting (e.g., park) and objects (e.g., dog bark, wind, birds chirping) they heard in each scene. Overall, object identification was more accurate and benefited more from longer scene durations than setting identification. Different low-level (pitch, frequency) and mid-level (spectrotemporal patterns) acoustic features predicted performance across the two tasks, suggesting that distinct but potentially interacting mechanisms support object and setting perception in natural scenes. In Experiment 2, participants completed separate forced-choice object and setting identification tasks during electroencephalography recording. Although no significant differences in neural activity were observed between tasks, the spectral complexity of scenes modulated the P2 event-related potential, indicating that processes relevant to both object and setting identification share a sensitivity to acoustic features at a mid-level stage of processing. Together, these findings suggest that object and setting identification may rely on partially overlapping mechanisms. Understanding how the auditory system integrates both object- and scene-level information offers insights into real-world listening and can inform the development of more effective artificial intelligence systems and hearing assistive devices for navigating complex environments
Bio-Inspired Electroactive Polymer (EAP) Sensors for Surface and Canal Flow Sensing in Dynamic Environments
No full textNature can often create some of the most efficient and elegant solutions to complex problems, and engineering stands to benefit greatly from these time-tested designs. One of the more sophisticated examples of this is the lateral line system in fish: a distributed network of superficial and canal neuromasts that enables aquatic species to detect fluid disturbances with remarkable precision. This dissertation leverages that biological framework to explore the potential of electroactive polymers (EAPs), aiming not just to replicate structure, but to emulate function.Two classes of EAPs form the basis of this work: electroactive plasticized polymer gels (EPPGs) and ionic polymer-metal composites (IPMCs). Both offer their own unique morphology and mechanoelectrical transduction (MET) mechanisms that make them well-suited candidates for mimicking the different components of an artificial lateral line. Their implementation is examined across two case studies: (1) a soft surface-mounted sensing skin inspired by superficial neuromasts, and (2) an internal sensor embedded within a synthetic canal structure, emulating the filtering behavior of canal neuromasts. The relationship between fluid dynamics, sensor geometry, and EAP response was explored through an integrated analytical, numerical, and experimental framework. The artificial skin successfully detected turbulent transitions at predicted thresholds, capturing nuanced spatial disturbances downstream. The canal sensor exhibited clear frequency-selective resonance around 90–110 Hz, matching analytical and simulation predictions. These findings demonstrate the feasibility of bioinspired EAP flow sensors and highlight opportunities to refine analytical models through experimental insights, enhancing real-world fluid sensing applications