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Spatiotemporal Impact of the Deepwater Horizon Oil Spill on Benthic Meiofauna Communities
No full textIn April 2010, the catastrophic Deepwater Horizon (DWH) oil spill began in the northern Gulf of Mexico. Millions of barrels of oil contaminants were expelled from the wellhead at a water depth of 1525 meters, in what would be one of the largest oil spills in history. A large portion of these contaminants remained in the deep-sea and severely impacted the benthic ecosystem, especially meiofaunal organisms. The initial impact of the DWH oil spill on these organisms, along with their subsequent recovery, were studied in 2010, 2011, 2014, and 2022. Thirty-four stations, consisting of eight highly impacted stations, eleven moderately impacted stations, six low impacted stations, and nine non-impacted stations, were consistently sampled to observe the effects of the spill on the benthic meiofauna community. Meiofaunal abundance was impressively high (i.e., disproportionately high nematode abundance reported from impacted stations) following the oil spill. From 2010 to 2022, abundance declined by approximately 80%, demonstrating a consistent decreasing trend, while meiofaunal diversity, as measured by Hill’s N1, increased over time. These trends suggest a gradual return toward pre-spill conditions. Furthermore, convergence of variables such as abundance, diversity, and the nematode to copepod ratio across impact levels over time provides preliminary evidence of recovery. Meiofaunal community composition showed distinct differences between stations impacted by oil and those without impact in 2010, 2011, and 2014, but showed no such variation in 2022. Principal Component Analysis (PCA) showed that oil spill impact and meiofaunal community structure were no longer directly related, suggesting that the spill is no longer the primary driver of community composition. These notable changes in meiofaunal abundance, diversity, community composition may indicate preliminary signs of recovery from the DWH oil spill
2023 Nevada Middle School Youth Risk Behavior Survey (YRBS): Urban, Rural, and Frontier Special Report
No full textCenters for Disease Control and Prevention, Nevada Division of Public and Behavioral Healt
Helping at the End-of-Life: A Phenomenological Study on the Experience of Pediatric Palliative Counselors
No full textPediatric palliative counseling is a branch of counseling that focuses on meeting the psychosocial, emotional, psychological, and spiritual needs of children with life-limiting illnesses and their families. The experience of pediatric palliative counselors is largely understudied, which means there is no awareness as to how these counselors can be better supported to protect themselves, their palliative care team members, and their clients. As such, the research questions of this study were: (1) What is the clinical experience of counselors in the pediatric palliative setting? (2) What meaning do pediatric palliative counselors derive from their work? These questions were investigated through a hermeneutic phenomenological approach to discover the clinical experiences of the counselors and uncover hidden meaning that they gain from it. Through in vivo and focused coding, the major themes identified were identified which include ethical considerations, immediacy, emotional labor, and spirituality. Some implications and areas of future research derived from the findings are explored in the discussion
Hierarchical Game Theory based Control for Large Scale Multi-Agent Systems: A Hybrid Reinforcement Learning Approach
No full textDistributed optimal control and coordination strategies for large-scale multi-agent systems (LS-MAS) operating under uncertainty have become increasingly critical due to the challenges of scalability, communication complexity, and computational intractability. Traditional control approaches face the curse of dimensionality as the number of interacting agents increases, making them unsuitable for real-time implementation in dense environments. To address these limitations, mean field game (MFG) theory has been employed as a scalable approximation tool, wherein the behavior of the overall population is represented through a probability density function (PDF). However, conventional MFG formulations often lack the ability to capture diverse coordination behaviors due to their reliance on a single global PDF, leading to suboptimal performance in heterogeneous or structured systems. To enhance flexibility and coordination efficiency, a hierarchical game-theoretic framework is introduced by decomposing the entire agent population into multiple interacting subgroups. Within this structure, inter-group and intra-group dynamics are governed through layered game formulations that incorporate both strategic leadership and population-level interactions. The framework enables scalable solutions to complex coordination problems such as flocking and formation, under uncertainty and in a distributed manner. To further improve coordination effectiveness, an extended formulation of MFG is developed to account for structural heterogeneity and asymmetric density constraints. A decomposition strategy is proposed to represent the global terminal behavior through a collection of subgroup-level distributions, allowing increased expressiveness and adaptability in control design. In addition, a reinforcement learning-based algorithm is designed to achieve distributed policy optimization within this hierarchical structure, enabling agents to adapt to changing environments with limited information exchange. Finally, a theoretical and empirical study is conducted to analyze the fundamental trade-off between coordination efficiency and computational complexity. Stability, convergence, and robustness of the proposed approach are validated through extensive simulations and comparative analysis
Monitoring Squirrels from the Sky: Applications of Uncrewed Aerial Systems-Based Bioacoustic and Vegetation Monitoring Regimes for Mohave Ground Squirrel
No full textThis research aimed to better understand the areas occupied by the Mohave ground squirrel (Xerospermophilus mohavensis) through the application of uncrewed aerial systems-based (henceforward UAS or drones) bioacoustic monitoring. The Mohave ground squirrel (MGS) is a threatened species under the California Endangered Species Act, and occupies a northwest corner of the Mojave Desert, one of the smallest ranges of any ground squirrel (Hoyt, 1972). Several difficulties have been observed in monitoring MGS populations, most notably the species’ short activity season, its solitary nature, and its small and shifting range (Best, 1995). Chapter one offers a new methodology for ascertaining MGS occupancy by recording MGS alert calls using microphones suspended below UAS. By using audio data of MGS alert calls and convolutional neural networks, this research was able to determine MGS presence based on captured calls towards the goal of supplementing ongoing live and camera trapping. Chapter two captured vegetation information within MGS ranges using UAS-based multiband imagery and developed a random forest vegetation classification model for the study area, allowing for a better understanding of the vegetation that is most common within MGS ranges. In conjunction with on-the-ground camera and live trapping, this research adds drone-based bioacoustic and vegetation monitoring methods that will allow for faster data gathering, a better understanding of the ways in which MGS exist on the landscape, and serve as an important tool in their future conservation
Nevada State Climate Office Drought Report March 2025
No full textThis report was created by the Nevada State Climate Office to provide a statewide drought summary for March 2025
Modeling and Analysis of Interaction Networks in Ecological Systems
No full textUnderstanding how networks form and evolve is an important question in many fields such as ecology, epidemiology, economics, and sociology. Studying the mechanisms of network formation can yield insight into which factors are involved in edge formation and network growth. This thesis explores network formation within ecological systems, focusing on herbivore-plant interaction networks using field data collected in Ecuador. Two modeling frameworks are considered: the repeated choice model (RCM) and the stochastic actor-oriented model (SAOM). The RCM treats network formation as a sequence of discrete choices, modeling herbivores' selection of host plants based on plant-level attributes such as leaf count. The SAOM models network evolution as a continuous-time stochastic process influenced by both individual traits and the existing network structure. This thesis presents a comparative analysis of these two approaches, highlighting their respective assumptions, estimation strategies, and shortcomings. Results from applying both models to empirical data are presented, offering insights into the dynamics of ecological network formation
Heap Leach Closure Strategies in Semi-Arid Conditions: A Data-Driven Approach at the Öksüt Gold Mine
No full textThis paper was presented at the Heap Leach Solutions Conference, October 19-21, 2025, Sparks, Nevada.Considering the heap leach facilities operating in semi-arid climates globally, the mining industry faces an increasing need to develop long-term closure strategies that effectively mitigate environmental risks in this climate setting. This paper presents a case study on the testing approaches taken to develop a closure plan for the Heap Leach Facility at the Öksüt Gold Mine, operated by Centerra Gold located in south-central Türkiye (Kayseri Province). A suite of technical investigations has been initiated to support sustainable closure strategies that rely on the use of locally available materials and natural attenuation processes, with the aim of minimizing long-term environmental liabilities and reducing closure costs. The study evaluates key site characteristics including hydrometeorology, hydrogeology, environmental geochemistry as well as the availability of suitable cover materials and project's proximity to nearby communities. The semi-arid climate, with low annual precipitation and high evaporation rates, is a critical factor influencing both the leachate generation potential and the selection of suitable closure techniques. Initial investigations have focused on characterizing the quality of the leachates generated from the heap residue, with the goal of identifying appropriate management strategies such as biological or chemical treatment, soil attenuation, evaporation, or the application of covers. The paper details the characterization strategy, including drilling and collection of representative samples, and describes the rationale for conducting laboratory-based column humidity cell tests. These tests are designed as part of a phased analysis to simulate long-term leaching behavior under site-specific conditions and provide critical data to support the selection and design of effective closure measures. The case study highlights the practical benefits of these investigations and their role in guiding the site's proactive closure planning process, and also offers scalable strategies for heap leach closure in other semi-arid regions, contributing to future research and sustainable mining practices. This initial testing and analysis will set a foundation for critical pilot-scale evaluations to further isolate key closure opportunities and optimize costs and risk
Nevada State Climate Office Quarterly Report June-August 2025
No full textQuarterly Report and Outlook for notable weather and climate in Nevada through September-October 2025. English Version
Governance of Information Markets: Elite Interviews for High-Risk Domains & Recommended Policy Regulatory Solutions
No full textEmerging technologies such as Artificial Intelligence (AI), Web3, and other decentralized technologies have become crucial security, economic, and regulatory policy topics in the fields of public policy and politics. Yet, it is well documented that many current technology and economic regulatory policies are outdated and too abstract to properly protect democracy and human rights. This dissertation provides a qualitative empirical content analysis, recommends policy outcomes, and synthesizes expert opinions from 82 specialists on a variety of emerging technology issues, offering recommended solutions for policymakers. The elite interviews and associated content analysis of 42 interview questions explore the interdisciplinary field of emerging technology by identifying which domains face the highest risks, the most common ethical challenges across domains, and potential social and policy solutions to mitigate the ethical and societal risks of emerging technologies. The recommended policy solutions from interviewees are proposed in six categories: open-source software policy, social media, ethical AI, antitrust monopoly regulation, the internet and personal data as public utilities, and healthcare-specific policies. Additionally, I then synthesize these policy recommendations from interviewees highlighting the most practically feasible policies for the USA. The theoretical and legal recommendations in this research are based on the empirical results of elite interview content analysis and inter-rater reliability Likert scale scoring conducted by three researchers trained in qualitative methods. Findings from this research normatively support the motivation to update current U.S. and global technological and economic regulatory policies, including stricter requirements for organizations and users utilizing emerging technology systems, to better protect users, democratic governments, and human rights