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A Brief History of Mining Reclamation on Diné Bikéyah (The Navajo Nation)
No full textA Brief History of Mining Reclamation on Diné Bikéyah traces the emergence and transformation of Diné-led reclamation from the 1970s through the early 2000s. This dissertation argues that mining reclamation on Diné lands was not solely a matter of environmental compliance, but a deeply situated practice of Indigenous governance, moral responsibility, and technical innovation. Drawing from archival documents, site visits, and oral history interviews taken from 2018 to 2025, this study presents an account of how Diné professionals redefined reclamation from within tribal departments and federal programs in two sections. Part one examines the coal reclamation era, focusing on the development of tribal regulatory authority under the Surface Mining Control and Reclamation Act of 1977 (SMCRA), including the founding of the Navajo AML Department (NAMLRD) and UMTRA Programs, detailing how Diné professionals crafted risk classifications, hazard inventory, and engineering plans rooted in both federal and tribal law and priorities. Part two draws on ethnographic data collected in the field and then shifts to explore how Diné reclamation workers approached restoration not only as technical labor, but as moral and spiritual work shaped by ceremonial knowledge, land-based ethics, and the intergenerational burdens of radiation exposure. Through the framework of Critical Reclamation Studies, this dissertation centers the voices of Diné professionals and positions reclamation as a long-term practice at the intersection of science and spirituality, where legal pluralism, healing, and cultural survival are enacted. It calls for looking through the positivist gaze and centering Indigenous law, memory, and spiritual accountability into narratives about federal-tribal interactions on post-extractive landscapes where mine cleanup occurs
Hybrid High-Level Synthesis: System-Level Optimization under End-to-End Latency Constraints
No full textEmbedded systems are increasingly ubiquitous in domains such as medical devices, autonomous vehicles, and the Internet of Things (IoT), where meeting stringent timing, energy, and area constraints is critical. Despite the widespread adoption of high‑level synthesis (HLS) to accelerate hardware design, conventional methods often fall short in incorporating precise timing specifications and handling the immense system‑level design space. To address these gaps, this dissertation introduces a comprehensive suite of methodologies that together advance the state‑of‑the‑art in embedded system design. At the core is a \textit{Hybrid High-Level Synthesis} (\textit{H-HLS}) framework that synergistically integrates \textit{state-based HLS} (\textit{SB-HLS}) with \textit{performance-driven HLS} (\textit{PD-HLS}) techniques, enabling explicit timing information to be embedded in state‑based models. Validated through case studies such as wearable pregnancy monitoring and biometric authentication systems, H‑HLS demonstrates significant gains in energy efficiency and reduced hardware area under strict timing constraints. With timing now made explicit, the next challenge lies in exploring the vast system‑level design space. We therefore propose the \textit{Pruned Genetic Design Space Exploration} (\textit{PG-DSE}) method. PG‑DSE leverages an effective pruning strategy combined with an elitist genetic algorithm to rapidly identify Pareto‑optimal configurations, as evidenced by experiments on synthetic benchmarks and an autonomous driving subsystem—achieving orders‑of‑magnitude reductions in complexity with notable performance improvements. Building on this foundation, our research extends to an \textit{End-to-End Design Space Exploration} (\textit{EtoE-DSE}) framework that explicitly incorporates system‑wide latency constraints. Through a latency‑estimation model, pathfinding algorithms, and frequency‑based segmentation, EtoE‑DSE efficiently optimizes energy, area, and end‑to‑end latency—yielding superior performance in complex applications such as autonomous driving. Recognizing that even the best exploration algorithms require representative benchmarks, we introduce \textit{Vaegan}, a novel generative machine‑learning approach. By employing Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), Vaegan generates high‑fidelity synthetic data that closely mirrors real‑world distributions, thereby enhancing the robustness of system‑level HLS design space exploration. Finally, looking toward the next horizon, the dissertation investigates the transformative potential of Large Language Models (LLMs) in HLS. It explores LLM‑based methodologies for generating hardware descriptions from natural language and C code and examines their impact on performance, power, and resource utilization. In a complementary effort, we propose a framework for LLM‑assisted hardware‑software co‑design of post‑quantum cryptographic algorithms, addressing the computational challenges posed by emerging security demands in the quantum era. Collectively, these contributions establish an integrated pipeline—spanning timing‑aware synthesis, rapid exploration, synthetic‑data generation, end‑to‑end optimization, and AI‑driven design assistance—that enhances the efficiency and accuracy of hardware synthesis under tight constraints and paves the way for next‑generation embedded systems
Foraging Decisions Under Uncertainty in Bumble Bees
No full textBumble bee colonies rely on decentralized decision-making by individual foragers, who must navigate environments filled with uncertainty. Foraging decisions are shaped not only by the average value of resources but also by variability in rewards and limited prior information. Behavioral ecologists have long studied how animals adapt to such challenges, yet it remains unclear how colony-level context, environmental complexity, and individual traits interact to shape decision-making in social insects. In this dissertation, I use lab-reared Bombus impatiens colonies to investigate how bees make foraging decisions when facing stochastic rewards, novel options, and biomechanical constraints. Across four experiments, my dissertation examines whether foragers respond flexibly to uncertainty, explore strategically to acquire information, solve novel problems in complex environments, and learn to land efficiently on unfamiliar flowers. Specifically: (1) The first experiment investigates if B. impatiens workers alter their preference for risky versus consistent rewards depending on colony life stage or size. Colonies in different phases of development (worker production vs. reproductive production) were used to simulate changing energetic priorities. In a controlled foraging task, bees chose between a flower with a consistent sucrose reward and one with a variable reward of equal mean value. I found no evidence that colony stage predicted foraging preference, and only a weak, non-robust effect of colony size. These results suggest that risk-sensitive foraging in bumble bees may not be tightly coupled to colony-level traits, at least under the experimental conditions tested. (2) The second experiment examines whether bumble bees engage in strategic exploration when information could improve future decisions. Using a two-armed bandit task in both a walking Y-maze and a flight arena, I manipulated bees’ prior exposure to each flower type and the number of choices they could expect to make (decision horizon). While models of optimal foraging predict that animals should increase exploration when information is more valuable, bees did not do so. Instead, they preferred familiar or previously rewarding flowers, even when these were less informative. These findings suggest that bumble bees rely on simple heuristics like reward history or familiarity, rather than explicitly valuing information gain in uncertain environments.
(3) The third experiment looks at whether foraging routines, individual responsiveness, and environmental complexity influence bees’ ability to solve novel tasks. Bees were exposed to artificial flowers requiring unfamiliar manipulations and were housed in either simple or complex foraging environments. I quantified how consistently each bee followed the same foraging sequence (routine-ness), their responsiveness to novel flower introductions, and whether they landed on and solved the novel tasks. Bees with more rigid foraging routines were less likely to innovate, while environmental complexity increased landing on novel flowers but did not affect problem-solving speed. Responsiveness showed interaction effects with environmental complexity, but no individual trait consistently predicted innovation across contexts. These findings highlight how environmental context and behavioral consistency interact to influence innovation in foraging bees. (4) The fourth experiment explored how flower orientation and structural features affect the landing performance of inexperienced bees. Naïve B. impatiens foragers were presented with artificial flowers that varied in orientation (horizontal vs. vertical) and the presence of a labellum (a protruding structure often thought to aid landing). Despite assumptions that these traits enhance foraging success, I found that landing success was uniformly low among inexperienced bees and was not improved by flower orientation or labellum presence. Some bees preferentially contacted the labellum, but this did not translate into more successful landings. These results suggest that floral morphology alone may not facilitate landing in the absence of prior experience, and that motor learning likely plays a crucial role in effective foraging behavior
Evaluating Effectiveness of Volume- and Trajectory-Based Methods to Enhance Traffic Signal Retiming and Coordination
No full textOptimizing traffic signals often involves a trade-off between complexity, data availability, and real-world feasibility. Existing simulation-based studies lack validation using field-collected data, limiting their real-world applicability. Additionally, few studies evaluate cost-effective hybrid optimization strategies that leverage varying levels of data availability in areas with data constraints. Signal timing plans based on static volume data are outdated and often fail to capture real-time traffic dynamics, numerous agencies rely on these plans due to resource restrictions. Scalable, cost-effective solutions are needed to help bridge the gap between theory and practice. Trajectory data offers more accuracy, but its utilization is limited by cost and infrastructure needs. This study explores and compares different strategies for improving signal timing on eight intersections along Avenue B, a congested arterial corridor in Yuma, Arizona. The strategies include: a volume-based method, a trajectory-based method using real-time probe vehicle data, and a hybrid method that integrates both. Five scenarios were developed and evaluated using PTV VISSIM, with performance measured at both the corridor and intersection levels, using metrics such as average travel time, delay, number of stops, stop delay, and percentage of arrivals on green. The baseline simulation model was calibrated using field data to ensure realistic outputs.The assessment of several scenarios indicated that those including trajectory data surpassed the others. The combination of volume-based splits with trajectory-informed offsets (VSTO) exhibited the most balanced and effective performance, achieving a decrease of 13.17% in average corridor travel time and 53.99% reduction in stop delay. It enhanced corridor-wide progression while also reducing intersection-level delay by 25.36%. The trajectory-based offset coordination method (TOC) also improved signal coordination and vehicle progression with a travel time and stop delay reduction of 17.17% and 46.85%, respectively. However, these enhancements came at the expense of side street performance, with intersections experiencing an average control delay increase of 33.65%. The volume-based optimization with speed-informed offsets (VSSO) effectively reduced localized delay at individual intersections yet did not provide consistent improvements on the corridor-level. Intersection delay declined by 23.81%, while average travel times and stop delay decreased by 1.3% and 9.54%, respectively. While not significant, VSSO slightly improves on platoon progression whereas the scenario that optimizes splits only using volumes (VSO) does not improve on the corridor level, still, VSSO reduces intersection delay by the same amount as the former. Overall, the hybrid method VSTO which integrates trajectories with volumes, proved to be the most adaptable, offering both improved progression and localized delay reduction, highlighting the value of integrating even limited real-time data into traditional signal timing workflows. This study demonstrates that effective signal optimization does not always require complex infrastructure, but rather effectively utilizing even limited availability of datasets and tools can deliver scalable, context-sensitive solutions for cities like Yuma. The findings aim to support transportation agencies in adopting realistic strategies that bridge the gap between theory and implementation
Advances in Breaking AES Encryption using Power-Based Side Channel Attacks and Machine Learning
No full textThe proliferation of embedded devices has led to ubiquitous communication, sharing of information, and much more. Unfortunately, the security of information transmission is constantly under attack by adversaries, with new vulnerabilities and attacks being constantly discovered. Cryptography primitives are essential to secure an embedded device by encrypting sensitive information, which makes it more challenging for an adversary to breach the system or access the secure information. The cryptography security primitives depend on the hardware used to implement them. While AES encryption, an industry standard, is resilient against brute force attacks and has wide compatibility across systems, a clever adversary can use physical artifacts emitted from the device, known as side channels, to profile and train machine learning-based models to retrieve sensitive information from a device. In this dissertation, we empirically show that AES implementations running on embedded devices are vulnerable to power-based side channel attacks (SCA). Firstly, we propose a multi-architecture data aggregation technique to profile power traces for a system with an embedded processor that is based on three types of deep neural networks (NN), namely, multi-layer perceptrons (MLP), convolutional neural networks (CNN), and recurrent neural networks (RNN). This is one of the first works to explore the inter-architecture portability of NNs for SCAs. With the proposed data aggregation methodology, the ANNs trained on one device can predict the AES key on an architecturally different device with a performance ranging between 98.1% and 99.9%. Secondly, we successfully target a 32-bit AES implementation (MbedTLS) using different 32-bit ARM Cortex (Cortex-M4 and Cortex-M0) microcontrollers (STM32F303, STM32L443, and STM32F051) via a power-based side channel attack (SCA). This is also one of the first works that quantitatively shows 32-bit microcontrollers running a 32-bit AES implementation are vulnerable to power-based SCAs. Another novelty of the research is that it uses complete power traces during training. Compared to previous approaches that specifically target the first SBox AES operation, our approach reduces data acquisition and preprocessing requirements by eliminating the need to isolate the SBox operations within the power trace. We further introduce several techniques to improve the performance of the ANNs using multiple power traces during evaluation. The trained ANNs performance to predict the correct AES key is between 94.6% and 100%
Expanding the Utility of Triazabutadiene Chemical Probes To Enable Their Use in the Study of Protein Dynamics via 19F NMR
No full textMitochondrial function is central to cellular energy production, metabolic regulation, and signaling, and its dysfunction is implicated in diverse pathologies such as neurodegeneration, cancer, and metabolic disorders. This dissertation aims to investigate and expand novel chemical biology strategies to create pathways to probe mitochondrial protein modifications and dynamics, employing bioconjugation approaches to facilitate selective protein labeling and targeted small- molecule delivery. The work is organized into four major chapters, each addressing critical aspects of mitochondrial biology, chemical probe design, and analytical methodologies.Chapter 1 provides an overview of mitochondrial structure and function, emphasizing the organelle’s roles in oxidative phosphorylation, calcium homeostasis, apoptotic signaling, and the biosynthesis of essential biomolecules. The discussion highlights how disruptions in these processes contribute to mitochondrial dysfunction (MD) and subsequent disease states, establishing the rationale for developing tools to interrogate mitochondrial biology. Additionally, strategies for targeting mitochondria in drug discovery are reviewed, with a focus on modulating mitochondrial dynamics as a promising therapeutic avenue. Chapter 2 introduces bioconjugation as a chemical technique for studying protein modifications, detailing the principles of probe design using aryl diazonium ions (ADIs) and their masked precursors, triazabutadienes (TBDs). This chapter explores the reactivity and selectivity of electrophilic probes that covalently modify nucleophilic amino acid residues—especially tyrosine—through azo coupling reactions. The advantages and limitations of native versus bioorthogonal labeling strategies are discussed, and a comprehensive comparison of analytical techniques is presented. Methods such as in-gel fluorescence, UV–Vis spectroscopy, mass spectrometry (LC–MS/MS), and nuclear magnetic resonance (NMR) spectroscopy are evaluated for their roles in confirming probe attachment and mapping modification sites at residue-level resolution. Chapter 3 focuses on the experimental application of these chemical probes. A para- trifluoromethyl-substituted aryl diazonium ion (p-CF3 ADI) is synthesized and employed as a model system to monitor protein and DNA modifications. The incorporation of trifluoromethyl groups enables sensitive detection via 19F NMR, a technique that benefits from fluorine’s 100% natural abundance and lack of endogenous background in biological systems. Studies in both cell lysates and purified protein systems, using myoglobin as a model, demonstrate the probe’s ability to report on binding interactions, conformational dynamics, and reaction kinetics, providing detailed insights into probe behavior and chemical reactivity. Chapter 4 outlines the design and synthesis of dual-functional triazabutadienes equipped with mitochondrial-targeting motifs, such as triphenyl phosphonium. This work extends the utility of ADI-based probes to spatially controlled applications, enabling the selective delivery of reactive species to the mitochondria. The synthesis and preliminary evaluation of these organelle-targeted probes lay the groundwork for future studies aimed at elucidating mitochondrial protein interactions and advancing therapeutic development. Collectively, this dissertation expands the chemical biology toolkit with novel bioconjugation strategies, offering robust platforms for investigating protein modifications, mitochondrial dynamics, and subcellular targeting. The findings promise to advance our understanding of mitochondrial biology and inform the design of next-generation therapeutics for diseases associated with mitochondrial dysfunction.Release after 08/27/202
The Bioavailability and Role of Organic Phosphorus to Marine Microorganisms
No full textMarine microorganisms are the foundation of ocean productivity, driving a significant flux of carbon into the ocean and producing nearly half of Earth’s oxygen. This intricate and diverse microbial world requires phosphorus to grow, yet in many oceanic regions, phosphate availability ranges from co-limiting to limiting. To cope with this scarcity, marine microorganisms use a variety of strategies, including the enzymatic hydrolysis of compounds within the organic phosphorus pool. Organic phosphorus encompasses both natural dissolved organic phosphorus (DOP) and synthetic compounds, including organophosphate esters (OPEs), introduced through anthropogenic activity. While DOP is known to support marine microorganisms’ growth and productivity, the bioavailability of different compound bond-classes within this chemically diverse pool remains poorly characterized. Additionally, although OPEs are increasingly detected in global oceans, their interactions with marine microorganisms are largely unknown.
Through laboratory and field experiments, this dissertation investigates the bioavailability and role of both natural and synthetic organic phosphorus to marine microorganisms. Chapter 1 examines DOP bond-class utilization by Synechococcus, revealing a clear preference for phosphoanhydrides over phosphoesters and phosphonates. Chapter 2 focuses on natural microbial communities in the Amazon River Plume, uncovering distinct DOP utilization patterns among diazotrophs, photosynthesizers, and picoplankton. Chapter 3 compiles the global distribution of oceanic OPEs, while Chapter 4 explores their bioavailability and transport, identifying the Amazon River as a major Atlantic Ocean OPE source. Together, this work advances our understanding of how organic phosphorus shapes marine microbial ecology and biogeochemical cycling by demonstrating that bioavailability is not uniform, but compound-specific. By moving beyond the view of organic phosphorus as a uniform pool, these chapters show that different phosphorus bond-classes elicit distinct microbial responses, with cascading effects on phosphorus acquisition, carbon and nitrogen cycling, and the environmental fate of both natural and anthropogenic compounds (see Dissertation Synthesis for more detail).Release after 08/21/202
Resilience and Wellness Among Entry-To-Practice Accelerated and Traditional Nursing Students
No full textBackground: Nurses who have developed resilience and wellness skills have a more positive outlook on education and decreased turnover rates. These skills can be built in nursing school and improve not only nurse outcomes but also nursing student outcomes. No current research exists on the differences in resilience and wellness scores between accelerated and traditional entry-to-practice nursing students. The two program types often use different curriculum delivery methods and program lengths, and faculty tend to use various teaching and learning principles. However, there is no research to support the idea that the two groups are fundamentally different.Purpose: The purpose of this study was to investigate levels of resilience and wellness in entryto-practice nursing students in accelerated and traditional programs. Aims: 1. Compare resilience and wellness scores between accelerated and traditional entry-topractice nursing students. 2. Determine the relationship between student characteristics associated with wellness and resilience. Methods: This study employs a cross-sectional descriptive research design among entry-topractice nursing students. Multiple t-tests and ANOVA tests were used to investigate levels of resilience and wellness in entry-to-practice nursing students in accelerated and traditional programs. Results: The study found there was no statistical difference in wellness or resilience levels between accelerated and traditional students. Statistically significant results indicated that mental health and chronic health conditions negatively impacted wellness and resilience for both groups. Conclusion: The data suggests there is no significant difference in resilience and wellness levels for accelerated and traditional nursing students
Accumulation Of Metals and Metalloids in Sediment and Aquatic Organisms of Ecuadorian Mangrove Forests and Implications for Human Health
No full textThis study aims to determine the levels of toxic metals and metalloids and their ecotoxicological implications in two protected mangrove forests in Guayaquil, Ecuador: the Reserva de Producción Faunística Manglares El Salado (RPFMS) and the Refugio de Vida Silvestre Manglares El Morro (REVISMEM). Anthropogenic activities such as agriculture, urban development, and aquaculture introduce contaminants to these coastal forests, posing a risk to the ecosystems. We utilized inductively coupled plasma-mass spectrometry (ICP-MS) to quantify toxic elements. In Chapter 1, we examined concentrations of toxic metal(loid)s in sediment, catfish (Bagre pinnimaculatus), Peruvian mojarra (Diapterus peruvianus), and mussels (Mytella guyanensis, Mytella strigata, and Mytella trautwineana). In addition, we conducted dietary intake surveys with residents of Puerto Hondo in RPFMS to assess potential exposure through a quantitative health risk assessment. Our analysis revealed that concentrations of Ag, As, Cd, Cr, and Pb in sediment samples exceeded the US National Oceanic and Atmospheric Administration (NOAA) “effects range low” (ERL) guidelines, while Cu, Hg, Ni, and Zn exceeded their “effects range median” (ERM) guidelines. ERL and ERM represent the concentrations above which adverse effects may begin to occur and at which effects are frequently observed, respectively. In fish samples, iAs, Cd, MeHg and Se levels surpassed the US Environmental Protection Agency (EPA) screening levels for unlimited fish consumption with higher concentrations observed in catfish for most samples. For mussel samples (M. trautwineana), only Zn levels exceeded the guidelines of the Food and Agriculture Administration of the United Nations (FAO). The estimated daily intake (EDI) values for inorganic As (iAs), hexavalent chromium (Cr(VI)), methylmercury (MeHg) and Pb were higher in Peruvian mojarra compared to catfish. The target hazard quotient (THQ) and the hazard index (HI) did not exceed the threshold value of 1 in both fish species and mussels. Cancer Risk (CR) values indicated a potential carcinogenic risk from iAs and Cr(VI) in Peruvian mojarra, and from iAs in catfish and mussels. For Pb, the margin of exposure (MOE) values were in the safety threshold area, concluding that there are no potential exposure risks for both children and adults. In Chapter 2, we compared concentrations of toxic metal(loid)s in sediment and mussels (M. strigata) between RPFMS and REVISMEM. We found that As, Cr and Cu exceeded the ERL and Ni exceeded the ERM for sediment in both reserves, with higher concentrations in RPFMS. Most metal(loid) showed significant differences between “within city limits” (RPFMS) and “city border area” (RPFMS), “near the city” (REVISMEM) and “far from the city” (REVISMEM). For mussels, all the elemental concentrations were below the FAO threshold values for M. strigata. With the exception of As, Cd, and Co, all toxic elements showed higher concentrations in “near the city” (REVISMEM) compared to the “city border area” (RPFMS). This project aims to enhance understanding of the ecological effects of pollutants on these mangrove ecosystems, providing a scientific basis to prioritize conservation efforts and inform public policy actions. Collectively, our results show that RPFMS has more polluted sites than REVISMEM, with higher levels of toxic metal(loid)s in sediments, fish, and mussels. iAs in both mussels and fish and Cr(VI) only in Peruvian mojarra present carcinogenic risks. Pb in fish and mussel species do not pose a health risk. Catfish and Peruvian mojarra exhibit a HI lower than 1, indicating that non-carcinogenic health risks are unlikely for the Puerto Hondo community
The Arizona Geological Survey Drill Core Repository: Publicly accessible precompetitive exploration data
No full textThe Arizona Geological Survey, as part of the reinvigoration of its mineral resources program, has reestablished the Arizona Geological Survey Drill Core Repository. The repository in its present form contains core from 367 boreholes from across Arizona, accounting for 397,695 linear feet of subsurface drilling. All available data with respect to each hole has been digitized and made freely available as part of an ESRI ArcOnline webmap for the exploration geology community and members of the public to view. Core is available for in-person examination with advanced notice.Documents in the AZGS Documents Repository collection are made available by the Arizona Geological Survey (AZGS) and the University Libraries at the University of Arizona. For more information about items in this collection, please contact [email protected]