22232 research outputs found
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New Building Permits in the Mountain West, 2024
No full textThis fact sheet examines data on new build permits from January 2023 to January 2024 in the five Mountain West States of Arizona, Colorado, Nevada, New Mexico, and Utah.“New Home Construction Statistics,” published by This Old House, reported percent changes on new builder permits and construction manufacturing costs and statistics across all 50 states
Structural Racism and Mass Shooting Events in Mountain West Cities, 2015-2019
No full textThis fact sheet presents data on the link between social and demographic metrics of principal cities in four Mountain West metropolitan statistical areas (MSAs): Phoenix, AZ; Tucson, AZ; Denver, CO; and Las Vegas, NV. This fact sheet also reports the number of people affected by mass shooting events (MSEs) as originally reported in “Association Between Markers of Structural Racism and Mass Shooting Events in Major US Cities,” published by The Journal of the American Medical Association (JAMA) Surgery. The original article derives data from 865 MSEs across 51 MSAs in the United States
Senior Health (Age 65+) in the Mountain West, 2024
No full textThis fact sheet presents senior health care statistics for the Mountain West states of Arizona, Colorado, Nevada, New Mexico, and Utah. The “2024 Senior Report – State Summaries,” study from America’s Health Rankings, United Foundation provided the data for all fifty states and Washington, D.C
The Healthcare Workforce in Nevada, 2024
No full textThis fact sheet presents data on the healthcare workforce in Nevada. The U.S. Bureau of Labor Statistics’ Occupational Employment and Wage Statistics Query System provides data on the amount of employment in all job sectors at the state and metropolitan statistical area (MSA) levels. This fact sheet focuses on employment and wage data for the State of Nevada, the Las Vegas-Henderson-Paradise, NV MSA, and the Reno, NV MSA as of May 2024
Enhancing Reliability of Internet of Things Systems Through Machine Learning-Based and Data Fusion Techniques
No full textThe rapid proliferation of Internet of Things (IoT) devices has led to an exponential increase in connected devices across various sectors. Each IoT network typically operates independently, focusing on specific applications and utilizing data fusion techniques to combine sensor-generated data within its network to make a decision. Alongside the growth and development of IoT, vision of sustainable ubiquitous environments, such as smart cities, smart agriculture and health, smart societies etc. is also growing rapidly. Thus, as the number of IoT devices continues to grow and their applications and vision diversify, there arises a pressing need to create a cohesive and intelligent ecosystem of intelligent independent IoT networks. However, IoT systems face significant challenges, particularly in ensuring data reliability and continuity in scenarios of data loss or failure. Such failures, arising from device malfunctions, network disruptions, or environmental factors, result in incomplete or unreliable datasets, which can severely impact decision-making processes and system functionality. Therefore, this dissertation addresses these challenges by proposing an innovative approach interconnecting independent IoT architectures to share resources and advanced synthetic data generation techniques to mitigate the effects of data failures.This dissertation first proposes a novel interconnected or cross-network IoT architecture, facilitating communication between independent IoT systems. This architecture enables seamless data sharing and fusion across systems, creating a robust framework for addressing data failures. The proposed architecture emphasizes flexibility, modularity, and interoperability, incorporating multiple logical layers such as perception, network, data fusion, and security layers. By interconnecting standalone/independent IoT systems, the framework reduces the reliance on individual network components and enhances overall system resilience. The architecture is designed to harmonize heterogeneous IoT networks, leveraging shared data resources to address single-point failures and reduce the need for redundant sensor deployments. To complement this architecture, the research further extends its study by developing advanced synthetic data generation techniques based on K-Nearest Neighbors (KNN) combined with Iterative Principal Component Analysis (IPCA). These methods leverage the proposed cross-network data fusion framework to create reliable synthetic data for addressing missing or unreliable datasets. Two distinct data fusion methods are presented: (1) fusion of the same feature type across networks and (2) fusion of highly correlated feature types. These approaches enable the system to compensate for missing data by utilizing information from other IoT networks sensing similar or related features. The proposed methods eliminate the limitations of traditional techniques reliant on historical or redundant data from the same network, offering a more robust and adaptable solution for dynamic IoT environments. Comprehensive experimentation validates the effectiveness of the proposed approaches using real-world IoT datasets collected from diverse geographical locations and environmental conditions. Results demonstrate that the KNN+IPCA method significantly outperforms state-of-the-art machine learning, statistical, and probabilistic approaches, achieving lower Root Mean Square Error (RMSE) values across all tested scenarios. Furthermore, the integration of the proposed cross-network architecture with synthetic data generation techniques enhances data reliability and system adaptability, even in highly heterogeneous and failure-prone environments. Thus, this dissertation advances IoT systems by addressing data failure challenges, enabling operational integrity, resource optimization, and accurate decision-making. By enhancing system resilience, this dissertation paves the way for robust, scalable, and sustainable IoT ecosystems
Using NotebookLM in the Undergraduate Classroom
No full textThe advent of AI tools like ChatGPT has complicated the foundational task of teaching undergraduates research literacy—how to read, synthesize, and explain scholarly work. Participants in this session will learn to use Google NotebookLM to actively support student engagement with academic sources. The session will showcase a redesigned major assignment from a 200-level communication theory course where NotebookLM was used to guide students through the analysis of scholarly journal articles. The session will explore NotebookLM\u27s essential features, detail practical assignment models, and provide strategies for ensuring authentic, independent learning even with AI assistance
AI-assisted Avenues for Linguistic Assessment and Intervention Methods in Speech-Language Pathology
No full textGraduate students in Speech-Language Pathology learn, refine, and utilize linguistic interventions to assist in the clinical remediation or management of language disorders. Large Language Models (LLMs) and associated multimedia AI platforms offer rich opportunities for graduate clinicians to integrate this technology into responsive intervention methods that help monitor and adjust for real-time fluctuations in client comprehension and expressive needs. Clinical educators often use clinical artifacts and problem-solving to contextualize learning for future clinicians in the classroom, but how can Artificial Intelligence augment this experience while optimizing for agency and integrity? One main objective of this session is to demonstrate how AI platforms can be capitalized to augment the translational nature of graduate students’ clinical training. This demonstration session details modeling and instruction across clinical artifacts pertinent to AI-assisted, responsive, linguistic intervention methods in the clinical context: receptive/expressive vocabulary demands, grammaticality judgment tasks, narrative scaffolds/story elements, literacy-based activity scaffolds, and pragmatic language (social-cognitive) targets. These clinical scenarios also highlight the intersection of linguistic interventions and response generation using associated AI products. Conference attendees will engage in intriguing discussions on the ramifications of utilizing the AI in the context of social-behavioral clinical training. Participants can expect to acquire a deeper understanding of the opportunities and intersections associated with AI technology and clinical education. Given that linguistic skill development is relevant across the curriculum at every layer of discourse complexity, the AI-assisted language activities in this demonstration session can be adapted to augment the study skills of higher education students in any field
U.S News & World Report 2025 Graduate Program Rankings, UNLV & UNR
No full textThis fact sheet presents 2025 graduate program rankings for the University of Nevada, Las Vegas (UNLV) and for the University of Nevada, Reno (UNR) based on the data published in spring 2025 by U.S. News & World Report. The graduate program categories reported include business, education, engineering, fine arts, law, medicine and health care, sciences and mathematics, and social sciences and humanities
Private Military Contractors: Assessing Their Impact on U.S. Military Effectiveness and Ethical Standards
No full textBackground & Objectives: Scale of outsourcing: \u3e180,000.00 troops Major providers: Blackwater and DynCorp • Research focus: magnitude of impacthttps://oasis.library.unlv.edu/durep_lightning/1044/thumbnail.jp
Live Entertainment Tax
No full textThe Live Entertainment Taxis a 9% tax that is exclusively for casinos, nightclubs, and venues that have a minimum occupancy of 200. The Live Entertainment Tax is managed by both the Nevada Gaming Control Board, which oversees events that are held at gaming establishments, and the department of Taxation, which handles events held at all other venues. Clark County is home to the Las Vegas Strip, which is where the majority of the revenue from this tax is collected.https://oasis.library.unlv.edu/durep_posters/1263/thumbnail.jp