ScholarWolf

Welcome to ScholarWolf, the institutional repository for the University of Nevada, Reno. Managed by the University Libraries, ScholarWolf is an open access database and the home of scholarly works by University members, including the electronic theses and dissertations of our graduate students, journal articles, conference presentations, and more.



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    Unraveling an uncertain future for the Great Basin: how climate, grass invasion, and wildfire interact to drive ecosystem function
    (2024) Strain, Maxwell Kay; Hanan, Erin; Blaszczak, Joanna; Leger, Elizabeth; Gustin, Mae; Verburg, Paul
    Drylands are the earth's largest terrestrial biome and are rapidly experiencing change. In drylands of the western United States, three of the most influential global change processes are shifting precipitation regimes, annual grass invasion, and wildfire. Understanding how these drivers interact to influence ecosystem function, and how such interactions may change in the future, can provide critical insight for building ecological resilience. This dissertation explores how changing moisture regimes interact with grass invasion and wildfire to modify three key cycles in the Great Basin: carbon (C), nitrogen (N), and water. This work bridges multiple spatial and temporal scales using empirical studies conducted in the field, greenhouse, and laboratory, along with simulation modeling. The work in chapter one used a greenhouse and laboratory experiment to identify how soil aridity influences root exudation by cheatgrass (Bromus tectorum), an invasive annual grass, alters the cycling of carbon (C) and nitrogen (N) in the soil. We observed that cheatgrass was able to uniquely access N in dry soils, and that changes in soil condition that implicate increased C and N cycling were caused when cheatgrass root exudates were experimentally added to soils. This work shows that root exudation by cheatgrass accelerates soil C cycling and suggests that it modifies N cycling and provides cheatgrass with unique access to N under dry conditions. When combined with seasonal dynamics and annual grass phenology, these mechanisms may provide cheatgrass with greater access to soil N compared to native species. In chapter two, I used simulation modeling to mechanistically test the effects of changing precipitation regimes and grass invasion on water and N dynamics at larger, watershed scales. Using the Regional Hydro-Ecologic Simulation System (RHESSys)—a process-based ecohydrologic model that couples the cycling of water, C, and N—I examined how precipitation variability and invasion influence stream discharge and N export. I simulated four climate scenarios with three levels of cheatgrass invasion in a factorial design. Streamflow decreased with both climate change and grass invasion, both of which increased rates of evapotranspiration. Watershed N export increased with climate change and invasion. This work can help us understand how change and annual grass invasion in the Great Basin may interact to influence ecosystem water supply and water quality. Chapter three investigated the role of aridity and burn severity on N retention after wildfire using a field and laboratory approach. I quantified how burn severity and aridity influence some of the key factors driving N uptake by plants and soil microbes after fire. Plant N uptake exhibited a non-monotonic relationship with burn severity, where sites that burned severely had suppressed N uptake for up to two years following fire. Dry conditions muted plant and microbial N uptake for all burn severities. N uptake was lowest after severe fire and when soils were relatively dry. Soil N that exceeded plant and microbial demand when N uptake is suppressed is likely more vulnerable to export. Using a conceptual framework, I hypothesize that postfire N loss is likely to occur at a lower burn severity when conditions are dry after fire. This hypothesis expands existing N saturation theory to better capture the role of fire and its interactions with soil aridity in drylands.
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    Grain Boundary Pinning Approach for Manufacturing High-Strength Nanocrystalline Aluminum Alloys
    (2024) Kushwaha, Amanendra Kumar; Menezes, Pradeep L.; Zhang, Jun; Misra, Manoranjan
    Nanostructuring is a commonly employed method to improve the mechanical properties of metals and alloys, but its usage is limited due to the instability of nanocrystalline (NC) materials. To address this challenge, the current study employs doping techniques to obtain a stable NC structure. In the present work, commercially pure aluminum (Al) powders were milled at cryogenic temperatures (a) without magnesium (Mg) and (b) with 5 wt.% of Mg powders for different durations. The unmilled and cryomilled powders were characterized to determine the changes in particle morphology, elemental composition, and crystallite size. The results showed changes in the morphology of powders and a reduction in crystallite size with the increase in cryomilling duration. Thereafter, the bulk samples using cryomilled powders were manufactured using two different methods (a) spark plasma sintering and (b) cold spray processes. The mechanical properties of the bulk samples were assessed by conducting Vickers microhardness, tensile, and fatigue tests. The tests were performed both at the University's laboratory and at an independent testing facility. The test results from both testing sources showed a significant improvement in mechanical properties for the Al-Mg bulk samples as compared to pure Al. The mechanism for the enhancement in mechanical properties as a result of crystallite size reduction and grain boundary strengthening by the addition of Mg dopant is discussed. The current study also examined the total energy consumption and manufacturing costs involved in the production process. The cost analysis revealed that manufacturing a kilogram of nanocrystalline aluminum alloy costs less than $90 inclusive of the energy costs.
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    Data-Driven Analysis and Topology-Aware Learning of Phasor and Waveform Measurements for Enhanced Situational Awareness in Power Systems
    (2024) Mansourlakouraj, Mohammad; Livani, Hanif
    Power grids are evolving with the integration of more renewable generation resources and different types of loads. This shift introduces new types of challenging events, oscillations, and controller responses, in addition to typical faults and outages. Additionally, phasor and waveform measurement devices are being increasingly used, measuring system variables such as voltage and current at a high reporting rate across the grid. This provides opportunities to enhance modern power grid monitoring during events and grid responses. Therefore, a fundamental question is how to analyze this valuable recorded data for practical power system monitoring and achieve better situational awareness. This dissertation is concerned with data-driven analysis of events and operational changes of assets by using statistical analysis, signal processing, and topology-aware learning methods. Events, abrupt changes and oscillations in power systems create specific signatures on the measurement signals, so effectively analyzing them helps enhance event detection, clustering, classification, and localization of their sources. These attempts, relying on the proposed methods in this dissertation, such as graph-based learning, short-time modal analysis, and statistical wavelet-based studies, can provide energy utilities with insight into the ongoing conditions in the power system. This can enable them to take proper actions, predict grid responses, and prevent failures at early stages, ensuring reliable and sustainable energy for people.
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    Exploring the Neural Foundations and Causal Mechanisms of Real-World Cognition
    (2024) Fairchild, Grant; Walsh-Snow, Jacqueline; Strother, Lars; Rudd, Michael; Lescroart, Mark; Jiang, Fang; Kidd, Thomas
    The brain evolved to perceive and to facilitate interaction with an environment consisting of real, three-dimensional objects, an environment in which humans and animals still live most of their lives. However, our knowledge of visual cognition and visual neuroscience is based predominantly on studies that have used 2-D images as visual stimuli. If behavioral and brain responses to real objects and 2-D stimuli differ, then many of the traditional frameworks of cognitive neuroscience may need to be reevaluated. Recent studies have in fact shown that real objects elicit different behavioral responses than images in many cognitive domains, including perception, action, memory, and attention. However, neither the neural correlates underlying these differences, nor the causal mechanisms responsible for these differences, have been extensively investigated. Across three studies, this dissertation explores the neural and causal bases of real-object effects. In the first study, I use fMRI to compare the effects of distance and size on brain representations of real objects vs. printed pictures. In the second study, I use fMRI to compare the neural correlates of recognition memory for real objects vs. computer images. In the third study, I use eye tracking to compare gaze patterns for real objects, 2-D images, and 3-D images. My behavioral data shows that real objects are better remembered than images, and that gaze towards real objects, compared to both 2-D and 3-D images, gravitates more towards object parts relevant to the current task. My neuroimaging data show that the functions of the dorsal and ventral visual pathways may be more integrated during viewing of real objects than of pictures, and that regions supporting memory retrieval and object-action associations respond differently during recognition memory of real objects vs. images. My results suggest that the different responses to real objects vs. images depend largely on the potential of real objects for genuine physical interaction. These findings have major translational implications in an era of increased screen-time dominated by artificial, digital objects. These findings also reveal the limitations of 2-D experimental stimuli and highlight the value of real-world experimental stimuli in providing a comprehensive characterization of naturalistic visual cognition.

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