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Landscape and Mechanistic Approaches to Describing Milkweed Habitat and Drought Tolerance and the Implications for Western Monarch Butterflies
No full textIt is vital that we understand the distribution and resilience of critical pollinator floral resources in the face of anthropogenic stressors. We utilized an unmanned aerial vehicle (UAV) remote sensing method along with the machine learning model you only look once (YOLO) to map floral habitat. We focused on detecting two milkweed species, narrowleaf milkweed (Asclepias fascicularis), and showy milkweed (Asclepias speciosa). These species are host plants for the declining monarch butterfly (Danaus plexippus). Utilizing our model, we quantified milkweed cover and isolated relationships between milkweed area and monarch presence. Additionally, we theorized that maternal effects could play an important role in milkweed’s ability to adapt to drought stress. To test this, we grew narrowleaf milkweed from a lineage previously exposed to varying grandmaternal climatic conditions and maternal drought experimentation. We used statistical modeling to explore potential maternal and grandmaternal effects on our greenhouse generations physiological response to drought
An Introduction to Academic Reading - Instructor Version
No full textThis in-class activity aims to introduce students to what gets called “strategic reading.” By framing reading as “strategic inquiry,” this activity advances the concept that academic reading is an interrogative process. However, this activity does not touch on close reading. Students will practice reading as strategic inquiry on Saira MehMood’s five page, 2024 peer-review article, “Anthropology Unbound: A Vision for a Flourishing, Inclusive, and Accessible Future.” Mehmood is writing into a conversation about the future of anthropology, utilizing her experience outside of the academy and her intersectionality to demonstrate the limits of anthropology’s contemporary academic disciplinaril
Exploring the role of insulin-like peptides in the Western tarnished plant bug Lygus hesperus
No full textDynamic Defense for Adaptive Resilience Against Emerging Threats in Microgrid Cybersecurity Games
No full textThe drive for decarbonization in the energy sector has necessitated the introduction of sustainability in the power industry. This can be accomplished by integrating more renewable energy sources via microgrids that allow localized generation and consumption of electricity. Microgrids rely on embedded devices and communication networks to achieve controllability. The interdependence of physical and cyber layers in such systems makes them vulnerable to process-level rootkit attacks that can manipulate system states to hinder the achievement of nominal functionality, leading to instability. Rootkits also tend to eavesdrop on nominal system behavior to learn how to hide their actions from the microgrid defender in an effective manner. These abilities can prove to be particularly lethal for the power grid, allowing the malware to achieve persistence. Despite their ability to create undetectable, long-term manipulations in the system, rootkits have not been studied adequately by grid cybersecurity researchers. To study process-level rootkits in an analytical manner, this dissertation models their interactions with the grid defender as a multi-stage, non-cooperative, zero-sum, Markov game. The Markov game formulation ensures that no explicit assumptions are made regarding the malware's behavior, to represent its stochastic nature in an accurate manner. Additionally, to enable the defender to formulate dominant strategies for maximum utility in this game, the dissertation presents a centralized deep reinforcement learning-based framework that utilizes the knowledge of physical laws to identify infected components and perturbs adjacency matrix elements to establish resiliency in an autonomous manner, without any operator supervision. The understanding of physical laws allows the reinforcement learning-based defense framework to be scaled up to larger grid sizes without imposing significantly higher computational overheads. Acknowledging the limitations of single-point failure of centralized deep reinforcement learning, the dissertation also presents the concept of a decentralized deep Q-network (DQN)-based framework where one DQN agent is deployed at each distributed energy resource (DER). Each agent in this framework is primarily concerned with achieving system recovery to defend its corresponding DER from manipulations from wide-area communication networks. Several simulation results are provided to demonstrate the action of the developed strategies in mitigating manipulations within the cyber-physical microgrid environment. Further, the dissertation provides several case studies showcasing the scalability of the proposed framework and its superiority over conventional defense strategies. To assist decision-making for individual DQN agents within the microgrid by identifying cyber-attacks, a federated learning-based Intrusion Detection System (IDS) is also developed. This IDS is a robust tool to identify specific manipulation templates that may be executed by rootkits. A framework is also presented to analyze the progression stages of malwares such as rootkits within the smart grid environment. This framework is meant to serve as a reference guide for security researchers to understand and thwart attempts of attackers who try to deploy rootkits within the grid at their very early stages, without limiting the defenders' capabilities to only stop them after manipulation begins
P.A.C.K. – Privacy and Anonymity through Cybersecurity Knowledge: Creating a Gamified Simulator for Anonymized Networks
No full textCompared to other computer science disciplines, cybersecurity is a relatively new focus. The need to secure protocols and systems was not accounted for during initial development, and modern infiltration, exploitation, and abuse methods were not considered. As our reliance on technology continues to grow, there is now an increased focus on securing these systems against attack. Due to this rapid increase in demand, there is a sizable gap between the number of necessary cybersecurity positions and the number of employees knowledgeable enough to fill them. This shortage places excessive strain on critical sectors such as healthcare, national security, and public safety. Global and national initiatives are currently working to increase the exposure of cybersecurity principles and best practices to both technical and non-technical workers; however, large gaps in comprehension still exist. This lack of awareness further complicates the exploration and understanding of niche cybersecurity fields such as anonymous networking. , contributing to investigation times. There is a fine line between infringing on individual privacy and enabling effective law enforcement capabilities, but it is essential that we increase both the awareness of anonymous networking systems and their technical architecture. Doing so is vital to ensuring public well-being and informed discussion. To make this information accessible to a wider range of learners, we propose P.A.C.K., a gamified simulator for the Tor and I2P anonymous networking applications. PACK provides a visual, interactive environment for upper secondary and novice learners to explore anonymous networking concepts with a low barrier to entry
Heat Waves, Early Birth, and Vulnerable Populations: Epidemiological and Spatial Analyses in the United States
No full textHeat waves are a serious and growing threat to human health and society. While there is no single definition of a heat wave, elevated morbidity and mortality during extreme heat is consistently observed in addition to other societal impacts, such as strain on the energy infrastructure, drought, and wildfire risk. This dissertation examines extreme heat events through a public health and equity lens. In Aim 1 we estimate acute effects of heat waves on preterm and early-term birth in eight US states from 1990-2017, adjusting for seasonal patterns of conception. In Aim 2 we evaluate if the acute effects of heat waves on early birth vary by individual-level factors (maternal age or education) or area-level factors (impervious land cover or social deprivation). In Aim 3 we evaluate spatial patterns in overlapping social vulnerability and heat in nine western US cities during the series of heat waves that occurred in June-July 2021.
For Aims 1 and 2, we obtained temperature data from the novel High-resolution Urban Meteorology for Impacts Dataset. Daily mean temperatures were extracted at the zip code tabulation area (ZCTA) level for up to 28 years and linked to birth records from eight US states: California, Florida, Georgia, Kansas, Nevada, New Jersey, North Carolina, and Oregon. Defining heat waves in multiple ways, we performed time-stratified case-crossover analyses to estimate acute effects of heat waves on preterm (<37 weeks) and early-term (37-38 weeks) birth. We calculated state-specific and pooled odds ratios (Aim 1). We adjusted for seasonal patterns of conception and error in last menstrual period reporting by including the probability of early birth among ongoing pregnancies at risk. In Aim 2 we conducted stratified analyses for both preterm and early-term birth to determine if the association between heat wave and early birth was modified by individual level and area-level factors: maternal age, maternal education, ZCTA-level land cover and ZCTA-level social deprivation.
The main analysis included 2,966,661 early-term and 945,869 preterm births occurring from May - September across the eight states from as early as 1990 to 2017. Results showed modestly elevated odds of early-term birth for heat waves occurring in the four days preceding birth. The adjusted pooled odds ratios across heat wave metrics ranged from 1.008 to 1.022 for preterm birth and from 1.007 to 1.020 for early-term birth. For the association of acute heat wave exposure and early-term birth, we found evidence of effect modification across all stratification variables (Aim 2). Stronger effects were evident among younger and less educated mothers as well as those living in areas of higher social deprivation and higher impervious land cover. For preterm birth, the highest heat-related risk was less consistently linked to socioeconomic disadvantage, as associations were also evident among those living in low deprivation areas, older mothers, and more educated mothers.
In the third aim, we investigated a series of heat waves that occurred in June-July 2021 in nine western US cities (Boise, Las Vegas, Los Angeles, Oakland, Portland, Sacramento, Salt Lake City, San Francisco, and Seattle) to determine to what extent vulnerable populations experienced a disproportionate burden of extreme heat. We defined vulnerability at the census tract level using the Agency for Toxic Substances and Disease Registry’s social vulnerability index (SVI) and linked to temperatures modeled at a 1km2 resolution to capture urban heat island gradients. We examined correlations between SVI and minimum and maximum temperatures averaged across heat wave days. We found that vulnerability was positively correlated with heat wave temperatures in most cities, the exceptions being Oakland and San Francisco. There was significant spatial clustering, or hotspots, in the same cities, of census tracts that experienced both high vulnerability and heat wave temperatures. Such areas may be appropriate targets for heat wave mitigation efforts, such as cooling centers or residential greening efforts.
Using both epidemiological and spatial analysis methods, this dissertation contributes to our knowledge of the effects of heat wave exposure on early birth risk and identifies populations most affected by heat waves
ROBUST AND ADAPTIVE VOLT-VAR AND DEMAND RESPONSE STRATEGIES FOR ACTIVE DISTRIBUTION NETWORKS UNDER COMPLEX UNCERTAINTIES
No full textThe rapid proliferation of distributed energy resources (DERs)—such as photovoltaic (PV) systems, battery energy storage systems (BESS), and flexible loads, including residential and commercial buildings with heating, ventilation, and air conditioning (HVAC) systems, as well as electric vehicle charging stations (EVCS)—has created tremendous opportunities for grid services while also introducing unprecedented operational challenges for active distribution networks (ADNs). This dissertation comprehensively addresses these emerging complexities through the development and validation of innovative optimization frameworks, integrating Volt-VAR optimization (VVO) and demand response programs (DRP) in ADNs, particularly within the context of commercial buildings. Recognizing the critical importance of accurate uncertainty representation, this dissertation adopts a Gaussian Mixture Model-based Chance-Constrained Optimization (GMM-CCO) approach. This methodology effectively characterizes complex, non-Gaussian uncertainties prevalent in PV generation, load fluctuations, and potential extreme conditions, thereby significantly enhancing the robustness and resilience of operational strategies. The GMM-CCO enables ADNs to withstand both typical forecast deviations and rare, high-impact scenarios, ensuring continuous and reliable performance without compromising operational efficiency. Further advancing the field of ADNs control strategies, this dissertation introduces an adaptive Q-V droop control methodology underpinned by an offline Extremum-Seeking (ES) algorithm. This novel approach leverages local Thevenin equivalent estimations, performed autonomously by edge processors at inverter nodes, thereby avoiding intrusive real-time network perturbations. The ES algorithm dynamically calibrates droop settings offline, producing stable, optimized reactive power injections. This significantly mitigates voltage fluctuations, improves power quality, and extends the operational lifespan of inverter-based systems. The decentralization of control processes reduces communication burdens, facilitating practical deployment in large-scale ADNs. The methodologies proposed in this dissertation have been rigorously validated through comprehensive simulations on widely recognized IEEE benchmark systems, including the IEEE 13-node, IEEE 37-node, IEEE 69-node, and IEEE 123-node test feeders. These diverse simulation environments confirm substantial improvements in key operational metrics, highlighting significant reductions in voltage deviations, network energy losses, and overall operational costs. The scalability and adaptability of the proposed solutions are evident from their consistent performance across various network sizes and configurations, demonstrating practical applicability and effectiveness in real-world scenarios. Additionally, this dissertation emphasizes targeted demand-side flexibility from commercial buildings. By strategically focusing on high-value commercial loads—such as HVAC and EV charging facilities—the dissertation offers practical insights into overcoming implementation barriers associated with broader demand response programs. This targeted approach simplifies coordination, reduces operational complexity, and maximizes the impact of demand-side management strategies, thereby enhancing both economic and operational outcomes for distribution system operators (DSOs). Overall, the proposed frameworks and methodologies offer robust and scalable solutions for the integrated management of voltage regulation and demand-side flexibility in modern ADNs. By leveraging advanced statistical uncertainty modeling, adaptive control techniques, and targeted demand response strategies, this dissertation significantly contributes to enhancing grid resilience, reliability, and operational efficiency, providing valuable insights and practical solutions to effectively manage the evolving landscape of distribution networks
Hydrogel-Particle Interaction-Powered Embedded Ink Writing: from Material Design to Biomedical Applications
No full textThis dissertation addresses two critical challenges in embedded ink writing (EIW): prolonged fabrication times and restricted printing feature sizes. Initially, advanced strategies for designing novel support bath materials tailored specifically for EIW applications are explored. By developing nanoclay-based hydrogel nanocomposites incorporating sodium alginate (NaAlg), polyethylene glycol diacrylate (PEGDA), and Pluronic F127, versatile control over rheological properties was achieved, addressing constraints in printable feature sizes and printing speeds. Nanoclay-Pluronic F127 composites demonstrated robust thermoresponsive behaviors, ideally suited for dynamic printing processes.Subsequently, fundamental mechanisms governing material interactions and filament formation essential for optimizing EIW processes were systematically investigated. Detailed analyses revealed how electrostatic, jammed, and polymer chain interactions influenced the microstructure and rheology of nanocomposites. Six distinct filament categories were identified, with a novel position-shape-size (PSS) evaluation framework introduced to comprehensively assess filament viability, enhancing precision and resolution.
Building on these insights, innovative EIW strategies—Multiscale Embedded Printing (MSEP) and High-Speed Embedded Ink Writing (HS-EIW)—were developed. MSEP enabled precise multiscale fabrication of complex organ structures, significantly improving dimensional accuracy and surface smoothness, exemplified by printing sophisticated anatomical models. HS-EIW dramatically reduced fabrication times by leveraging optimized nanoclay-hydrogel support baths, demonstrated by the rapid production of anatomically accurate human kidney analogs.
Finally, these advancements were successfully translated into critical biomedical applications, notably surgical planning and diagnostic precision. The developed stimuli-responsive support baths facilitated fabrication of patient-specific brain tumor models and realistic lung cancer diagnostic tools, improving surgical outcomes and diagnostic accuracy. Future research will focus on further refining biocompatibility and incorporating living cellular components, thereby enhancing the translational potential of these technologies in personalized medicine and regenerative therapies
Analyzing the Research and Ethical Implications of Shell Midden Data Collection Methodologies
No full textShell middens are archaeological features constructed by past settlers of coastal and aquatic regions containing discarded mollusk shells, faunal bones, and artifacts. Since the 19th century, archaeologists have used midden materials to research diet and human-environmental interactions by quantifying faunal assemblages. This is typically done using disparate methods including weight data, the count of the minimum number of individuals (MNI), or the count of the number of identifiable specimens (NISP), among other methods. Archaeologists can also conduct specialized analyses such as radiocarbon dating or stable isotope analysis to gain insights from the collection that cannot be easily extracted. Shell size measurements have also been used to reconstruct paleoclimate and interpret harvesting strategies. Despite this wide range in research areas and methodologies, laboratory processing procedures differ between archaeologists, resulting in data that can be difficult to compare between regions because of a lack of standardization. In addition to affecting our ability to compare patterns between sites and regions, this reduces research accessibility and compounds the archaeological curation crisis and the growing number of orphaned collections.In this thesis, I conduct a literature review of shell midden research from the Northern Channel Islands (NCI), mainland Alta California, the Eastern United States, Australia, and Baja California to determine which suite of primary data extraction methodologies enables the greatest breadth of research. I use my results from the literature review to develop a standard operating procedure (SOP) that I tested in a case study of four shell midden sites from Santa Rosa Island, California. I make site comparisons based on faunal data and make observations on the potential of the research that comes with using these primary data measurements.
Through my research, I determine that collecting weight data, MNI, NISP, whole shell measurements, biological proxy measurements or reconstructions, and identifying specimens eligible for specialized analyses enables the greatest scope of shell midden research. I argue that following a laboratory SOP generates a collection of data that is resistant to changing trends in shell midden archaeology and allows for comparisons in data between sites and regions. Furthermore, following a protocol decreases the likelihood of the collection becoming orphaned and mitigates the contribution to the ongoing curation crisis faced by collection managers and reduces the amount of worked required for Native American Graves Protection and Repatriation Act (NAGPRA) compliance. Finally, producing a comprehensive set of primary data increases the accessibility of the collection for future researchers, tribal members, and students interested in conducting research. I intend for my developed SOP to be used to train archaeological laboratory student workers and shell midden archaeologists worldwide
Summary of Virtual Site Visit with the Utah Department of Transportation (Memorandum D)
No full textThis effort aimed to conduct a comprehensive gap analysis on the use of high-polymer (HP) binders and mixtures, identifying critical limitations, gaps, and needs through a Strengths-Weaknesses-Opportunities-Threats (SWOT) framework. In addition to addressing these gaps, the scope included documenting effective practices and lessons learned by state Departments of Transportation (DOTs). The findings provided DOTs with valuable guidance for designing, constructing, and accepting HP binders and mixtures, complementing work completed under the FHWA EDC-6: Targeted Overlay Pavement Solutions (TOPS) program. To achieve this objective, information was gathered through virtual site visits and other outreach methods with five key agencies, including a session graciously hosted by the Utah Department of Transportation (UDOT).Federal Highway AdministrationUnited States Department of Transportatio