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Analytic and holomorphic structures in Lie groupoids, algebroids, and Poisson geometry
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo termsThe student, Ning Jiang, accepted the attached license on 2025-07-14 at 13:10.The student, Ning Jiang, submitted this Dissertation for approval on 2025-07-14 at 13:17.This Dissertation was approved for publication on 2025-07-16 at 15:57.DSpace SAF Submission Ingestion Package generated from Vireo submission #22502 on 2025-10-20 at 20:15:09This thesis explores analytic and holomorphic structures in Lie groupoids, Lie algebroids, and Poisson geometry. The first chapter provides background material on Lie groupoids, Lie algebroids, and Poisson structures. In the second chapter, we prove that any analytic s-proper Lie groupoid is analytically linearizable. Our approach relies on constructing an analytic Haar density and an analytic 2-metric on groupoids. The third chapter introduces the notion of complexification for analytic Lie algebroids. We establish that when the anchor map is injective or surjective, the integrability of the original algebroid implies the integrability of its complexification. Moreover, if the analytic algebroid is of s-proper type, its complexification is locally integrable. In the fourth chapter, we develop a computer program for computing the holomorphic Poisson cohomology of projective spaces
Virtual study sessions for adult literacy education teachers’ multilingual awareness
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo termsThe student, Elif Varlik, accepted the attached license on 2025-07-18 at 15:08.The student, Elif Varlik, submitted this Thesis for approval on 2025-07-18 at 15:18.This Thesis was approved for publication on 2025-07-22 at 10:20.DSpace SAF Submission Ingestion Package generated from Vireo submission #22658 on 2025-10-20 at 20:15:27LESLLA (Literacy Education and Second Language Learning for Adults) teachers need more professional development (PD) as they encounter challenges in applying multilingual and culturally responsive strategies in practice (Floyd, 2022; Wiseman, 2024), especially given that learners’ limited literacy and metalinguistic awareness further challenge instruction (van de Craats et al., 2006). While PD programs for literacy teachers exist (Kiramba & Trainin, 2025; Tammelin-Laine et al., 2021), they often omit reflective practice on critical multilingual awareness—a gap that research suggests can be filled through contextualized learning and collaborative inquiry (Farrelly, 2017; Maynard et al., 2024; Vinogradov & Liden, 2009). To address this gap, this multiple case study explored the perceptions and reflections of three LESLLA teachers in North America, examining how virtual PD sessions on critical multilingual awareness enhanced their preparedness. Data was collected through pre- and post-questionnaires and semi-structured interviews to capture changes in beliefs, perceptions, and practices. Over the course of six weeks, teachers participated in weekly Zoom sessions, reflecting on topics such as trauma-informed pedagogy, translanguaging, identity texts, pleasure reading, technology integration, and gender issues. Data were analyzed thematically using an eclectic coding approach, inspired by Charmaz’ (2014) constructivist grounded theory, which involved multiple cycles of coding leading to the generation of themes (Creswell, 2013; Saldaña, 2021). The analysis was further guided by Liu’s (2013) critical reflection framework and Farrell’s (2015) concept of reflective practice to explore the development of pedagogical thinking. Additionally, the lens of critical multilingual language awareness (García, 2015) was used to examine participants’ engagement with language ideologies and multilingual practices. The findings revealed that the study sessions enhanced participants’ awareness of multilingual strategies and linguistic complexities in LESLLA classrooms, while also uncovering shared challenges and effective practices. Although the participants were already experienced teachers, they reported that the sessions enriched their practice through peer learning, exploration of underexamined topics, and exposure to diverse teaching experiences of other participants. In particular, the three LESLLA teachers demonstrated strong expertise in using identity texts, technology, and translanguaging. However, they reported fewer strategies and more uncertainty when addressing trauma-informed pedagogy and gender-related barriers. Pleasure reading emerged as another area where teacher knowledge and strategy use varied, in contrast to their more established skills in phonics and reading instruction. Post-study reflections highlighted the value of fostering collaborative, reflective learning environments to support sustained professional dialogue. These findings inform the design of responsive, practice-based PD tailored to the needs of LESLLA educators and institutions
Development and testing of a modular cubeSat system enabling rapid access to space
No full textSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo termsThe student, James Helmich, accepted the attached license on 2025-07-18 at 17:25.The student, James Helmich, submitted this Thesis for approval on 2025-07-18 at 17:40.This Thesis was approved for publication on 2025-07-21 at 16:08.DSpace SAF Submission Ingestion Package generated from Vireo submission #22663 on 2025-10-20 at 20:15:28This thesis presents the development of a modular CubeSat system, herein called CUBE, that provides configurable payload hosting capabilities and promotes rapid access to space. Small satellites are being increasingly utilized to operate civil, defense, commercial, and educational payloads in space. These satellites offer comparatively easy access to space for payload developers. However, current standard program practices are inefficient and create many opportunities for irrecoverable mission failures in the leadup to launch. Each new payload typically requires a bespoke satellite to support it. The assembly, integration, and testing process is highly serialized, leading to schedule delays and late discovery of integration issues. All of this results in large monetary and schedule burdens on any organization wishing to launch a payload. The novel modular system developed for this thesis streamlines the assembly, integration, and testing process by dividing the structure into independent modules that separate the payload from the supporting bus hardware. The bus hardware selected for the system can be used to support a variety of different payloads. Furthermore, the modules can be configured in different orientations to meet the specific needs of a wide range of payloads. This allows the modular system design to be used for multiple missions, removing the need for bespoke satellite design work for each payload. The bus modules can also be flight qualified and prepared in advance, allowing for rapid response missions that can accommodate late payload integration. The versatility of the system is validated with a set of conceptual design reference missions. The design of the structure is then verified by a suite of load analyses. Once verified, a prototype of the structure is manufactured. The structure undergoes a thorough testing campaign to qualify it for spaceflight
Endocrine responses to alcohol ingestion in women with and without metabolic surgery
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo termsThe student, Mariel Molina Castro, accepted the attached license on 2025-07-23 at 10:34.The student, Mariel Molina Castro, submitted this Thesis for approval on 2025-07-23 at 10:41.This Thesis was approved for publication on 2025-07-23 at 14:49.DSpace SAF Submission Ingestion Package generated from Vireo submission #22704 on 2025-10-20 at 20:15:41Glucagon-like peptide-1 (GLP-1)-based therapies, widely used for treating obesity and type 2 diabetes, also show potential for reducing alcohol-seeking behavior. However, the effects of alcohol on endogenous GLP-1 and related peptides remain poorly understood, especially in individuals who have undergone metabolic surgery, a group at elevated risk for alcohol use disorder (AUD) despite an enhanced GLP-1 response post-surgery. This work integrates findings from cross-sectional and longitudinal placebo-controlled studies that examined the acute endocrine and glycemic effects of alcohol ingestion in women with and without a history of metabolic surgery. In the cross-sectional study, 18 post-surgical and 14 control women were assessed for hormonal and metabolic responses to an alcohol dose. Alcohol decreased GLP-1 by 34% in both groups and reduced ghrelin more in controls (27%) than in the surgery group (13%). Alcohol modestly lowered glucose and transiently increased insulin secretion in both groups. However, 28% of post-surgical women reached hypoglycemic levels, compared to none in the control group. In the longitudinal arm, seven women underwent identical alcohol/placebo challenges before and ~5 months after surgery. We observed that post-surgery, blood alcohol concentrations (BAC) peaked faster and higher, with ~28% lower alcohol clearance, likely reflecting the loss of fat-free mass. However, the acute decrease of GLP-1 and marked increase of fibroblast growth factor 21 (FGF21) following alcohol intake did not significantly differ before and after surgery. Similarly, alcohol-induced glucose reductions were not more pronounced postoperatively. Together, these findings confirm that acute alcohol consumption reduces endogenous GLP-1 and increases hypoglycemia risk after metabolic surgery. They also suggest that, despite substantial weight loss and improvements in insulin sensitivity post-surgery, acute endocrine responses to alcohol in a fasted state remain largely comparable. These insights support the relevance of gut–brain and liver–brain peptide systems in understanding alcohol's effects and AUD risk in surgical populations
Improving radiation resistance of metal alloys through the addition of multiple synergistic solutes: Atomistic simulations and experimental analysis
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01The student, Soumyajit Jana, accepted the attached license on 2025-05-02 at 13:28.The student, Soumyajit Jana, submitted this Dissertation for approval on 2025-05-02 at 13:46.This Dissertation was approved for publication on 2025-05-08 at 07:19.DSpace SAF Submission Ingestion Package generated from Vireo submission #22158 on 2025-10-21 at 10:05:14Many radiation damage phenomena are driven by the production and fluxes of point defects, particularly in alloys. The addition of vacancy trapping solutes is one commonly known method used to improve radiation damage tolerance. However, this method has its limits, therefore in this work a method to ameliorate these limits has been studied. A novel approach for imparting radiation resistance to dilute alloys is proposed here whereby two synergistic solute species are employed, a first one, solute B, that binds strongly to vacancies and a second one, solute C, that binds to solute B and is also a slow diffuser in solvent A. This combination results in B-C solute clusters that are immobile traps for vacancies. These traps promote point-defect recombination over irradiation doses far beyond that achievable in binary alloys, where solutes that strongly bind to vacancies are typically fast diffusers and thus quickly removed from grain interiors by radiation-induced segregation. A parametric study was performed using Atomic Kinetic Monte Carlo (KMC) simulations to study the effects of the interplay between the various thermodynamic, kinetic and microstructural parameters that govern the evolution of such ternary alloys under radiation. This was then used to identify promising ternary alloy systems. One such system was studied experimentally and the beneficial effects of the 2 synergistic solutes were then demonstrated under ion irradiation. In the KMC simulations, defect clusters were not allowed to form to simplify the analysis of the effects of the multiple solutes. Therefore finally, molecular dynamics simulations and Onsager transport theory calculations were used to expand upon the KMC simulations to study how defect clusters like divacancies affect radiation-induced segregation in the Cu-Ag system. These showed that addition of trapping solute Ag greatly reduces divacancy mobility. The fractional reduction is much greater than one obtained when monovacancies are trapped by Ag. A similar observation might be observed when a second solute is added, which has been left for future work. Thus the strategy proposed in this work of combining distinct synergistic solutes should thus be highly beneficial too when divacancies and larger vacancy clusters are present
Analysis of hierarchically polarized communities on social media
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01The student, Dachun Sun, accepted the attached license on 2025-07-01 at 17:04.The student, Dachun Sun, submitted this Dissertation for approval on 2025-07-01 at 17:11.This Dissertation was approved for publication on 2025-07-08 at 09:27.DSpace SAF Submission Ingestion Package generated from Vireo submission #22395 on 2025-10-21 at 10:05:37Social media platforms have profoundly transformed public discourse, providing a global forum for individuals to share information, express opinions, and build communities. While they foster unprecedented connectivity, they also exacerbate polarization, forming communities characterized by hierarchical ideological divisions. Many existing computational approaches oversimplify these dynamics, reducing polarization to binary oppositions and neglecting the nuanced, evolving structure of beliefs within online networks. Furthermore, there has been limited exploration into predicting community responses to new hypothetical posts, leaving critical gaps in the understanding of and strategies for mitigating polarization. This dissertation addresses three central research questions: How can hierarchically polarized communities be identified and characterized on social media platforms? How can the collective responses of these communities to new social stimuli be realistically simulated, capturing their structural complexity and the cognitive mechanisms that drive opinion formation? And how can the discovered solutions be implemented in a practical tool? To answer these questions, a suite of computational techniques is developed to detect, represent, and simulate complex community structures in social media environments. A dynamic polarized belief representation framework grounded in recurrent graph autoencoders is introduced, allowing for the tracking and analysis of hierarchical community structures as they evolve over time. To address the scarcity of labeled data, a perturbation-based active learning strategy is proposed to optimize label efficiency in semi-supervised settings by strategically selecting and labeling informative nodes within the social graph. This dissertation also advances the state of the art in community response generation by developing a retrieval-augmented generation (RAG) framework that leverages both historical social media responses and external knowledge to forecast community reactions to hypothetical posts. To better reflect human biases, cognitive mechanisms such as memory recency, frequency, and similarity weighting are incorporated, emulating human biases in opinion dynamics and bridging the gap between rational computation and real-world behavior. Much of this work is integrated into an analytics tool for conflict monitoring and intervention, validated through demonstrations on real-world social media datasets. Overall, this dissertation explores these core issues related to identifying, understanding, and simulating the evolution and responses of hierarchical and dynamic polarized communities on social media, thereby supporting efforts to address the challenges of polarization and radicalization in online environments
Beyond adoption: understanding how systems thinking and holistic management lead to long term cover cropping success
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01The student, Shae Cilento, accepted the attached license on 2025-07-02 at 15:20.The student, Shae Cilento, submitted this Thesis for approval on 2025-07-02 at 15:32.This Thesis was approved for publication on 2025-07-25 at 16:02.DSpace SAF Submission Ingestion Package generated from Vireo submission #22403 on 2025-10-21 at 10:05:38Despite growing interest in sustainable farming practices, cover cropping remains underutilized in Illinois, with only 3.35% of total farmland adopting the practice as of 2022 (National Agricultural Statistics Service, 2022, Table 47 and Table 1). Existing messaging and support systems have proven insufficient in facilitating both initial adoption and long-term implementation. This study examines strategies to reduce the learning curve of cover crop management and enhance sustained adoption by analyzing field-level data from Precision Conservation Management’s Five-Year Transition Program and conducting semi-structured interviews with experienced cover crop farmers and technical advisors. While prior research often frames cover crop adoption as a binary decision, focusing primarily on initial adoption drivers, this study shifts the perspective toward systems-level changes necessary for long-term implementation and profitability. Expanding on Roesch-McNally (2017), which analyzed how farmers navigate field-level and market barriers, this research identifies key management strategies that enable successful cover crop transitions and sustained economic viability. Findings emphasize the importance of systems thinking in cover cropping success. Farmers develop adaptive management strategies through experience, refining operational timing and decision-making to maximize cover crop benefits while minimizing risks to cash crops. A growth mindset, accountability, and adaptability enable growers to leverage ecological advantages to offset costs. Without these strategies, simply integrating cover crops into conventional systems remains financially unviable, as marginal yield increases fail to offset additional costs. Farmers identified three primary learning methods essential for success: guidance from conservation agronomists, support from farmer networks, and trial-and-error experimentation. Early mentorship provides a foundational understanding, while autonomous experimentation allows farmers to tailor strategies to specific field conditions. To promote further adoption and long-term success, industry and policy reforms must strengthen technical support, enhance agronomist training in advanced cover crop management, and expand research on product interactions within cover crop systems. Additionally, clear market incentives—such as premium pricing linked to corporate sustainability commitments and carbon markets—can mitigate early adoption risks. Conservation messaging also requires restructuring, as current approaches are overly politicized and fail to highlight the direct benefits of cover crops to farmers. Reframing cover crops as practical tools for increasing control and resiliency in an increasingly unpredictable agricultural industry may improve engagement, emphasizing their role in mitigating market volatility, erosion, extreme weather, and rising input costs – the primary adoption drivers cited by growers
Searches for supersymmetric particles and ATLAS track triggers
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01The student, Yumeng Cao, accepted the attached license on 2025-07-07 at 11:01.The student, Yumeng Cao, submitted this Dissertation for approval on 2025-07-07 at 11:33.This Dissertation was approved for publication on 2025-07-07 at 15:44.DSpace SAF Submission Ingestion Package generated from Vireo submission #22426 on 2025-10-21 at 10:05:41The Standard Model of particle physics has profoundly shaped our understanding of the fundamental forces and constituents of the universe. Despite its success, the model's inability to incorporate gravitational interactions and dark matter indicates significant gaps in our understanding of the universe's underlying structure. The quest for a more encompassing theory has led to the exploration of Supersymmetry (SUSY), a promising extension of the Standard Model that hypothesizes the existence of superpartners for every known particle. The discovery of SUSY particles would not only revolutionize our understanding of the fundamental particles but also provide critical insights into the dark sector of the universe. The Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) is at the forefront of this search. Operating at unprecedented energies, the LHC offers a unique window into physics beyond the Standard Model. Within this global endeavor, the ATLAS collaboration plays a pivotal role by probing new physics through its versatile detector system designed to capture a wide array of particle interactions. My PhD research is conducted within the framework of the ATLAS collaboration, focusing on the experimental search for SUSY particles and enhancing the capabilities of the ATLAS detector through technological innovations. This dissertation presents detailed accounts of my participation in searches for SUSY in 2 lepton and jets final states during the LHC Run-2 and in displaced lepton scenarios in Run-3. These searches target events where SUSY particles decay into final states that include leptons and jets, exploiting unique signatures that differentiate them from Standard Model backgrounds. The complexity of these searches requires the development and application of sophisticated data analysis techniques. Parallel to these experimental searches, this dissertation also details my contributions to the development of the ATLAS track trigger system. The enhancements to this system are critical for the detector's ability to manage and analyze data at the high event rates encountered in LHC Run-3 and the upcoming Run-4. Improvements in the track trigger system directly translate to better real-time event selection capabilities, which are essential for isolating rare physics events from the vast datasets generated by the collider. The methodologies employed in this research combine cutting-edge particle physics analysis techniques with state-of-the-art simulation tools and detector technologies. This comprehensive approach not only advances the search for SUSY but also contributes to the broader field of particle physics by enhancing the experimental techniques used in high-energy physics research. The results of these searches have significant implications for the field of particle physics. They either set stringent limits on SUSY theories or could potentially lead to discoveries that would necessitate a reevaluation of our current understanding of the physical universe. As the LHC continues to operate at higher luminosities and energies, the research documented in this dissertation will serve as a cornerstone for future explorations in the field
Advances in automated plant phenotyping and target-driven visual navigation for breeding candidates
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01The student, Junzhe Wu, accepted the attached license on 2025-07-11 at 15:17.The student, Junzhe Wu, submitted this Dissertation for approval on 2025-07-11 at 15:34.This Dissertation was approved for publication on 2025-07-16 at 09:33.DSpace SAF Submission Ingestion Package generated from Vireo submission #22524 on 2025-10-21 at 10:05:52Advances in agricultural robotics are transforming field operations by enhancing perception and planning capabilities, particularly in automated plant phenotyping and visual navigation. Leaf angle, a critical factor for solar light absorption and photosynthetic efficiency, directly impacts the growth and yield of row crops such as maize. As such, plant breeders are interested in selecting specific leaf angle traits, which require measurement of leaf angle. However, traditional manual measurement of leaf angle in field settings is nearly impossible due to the significant labor involved. Furthermore, measurement tactics, such as with protractors, do not scale to field settings, and the results have been found to vary widely between different individuals. In this thesis, we present a field robotic system equipped with low-cost sensors to automatically measure the angle of the leaf. The proposed method is capable of producing quantitative and qualitative estimates of leaf angle for numerous maize leaves consistently using an RGB-D camera. Our method provides a high-throughput low-cost tool for the measurement of leaf angle and is expected to enable new agricultural research. Additionally, autonomous navigation in unstructured and dynamic outdoor environments is critical for field robotics applications. For example, the breeding candidate after phenotyping needs to be located and their seeds collected for further breeding. Traditional Deep Reinforcement Learning-based visual navigation techniques face challenges in outdoor settings, particularly in the absence of high-resolution maps and GPS signals. This thesis presents a deep reinforcement learning-based approach for target-driven visual navigation in outdoor settings, using the successor feature (SF) framework to enhance the model's generalization and transfer learning capabilities. Our method constructs a grid-world environment for navigation tasks and employs a goal-conditioned reinforcement learning (GCRL) strategy that leverages SFs to capture environmental dynamics. This approach enables the model to transfer knowledge across tasks, making it adaptable to new environments with zero-shot or few-shot tuning. The experimental results demonstrate the adaptability of our method in new outdoor environments within the same domain. Furthermore, although the model is trained in a discrete grid-world environment, it is successfully deployed in real-time across different seasons within the same area, highlighting its robust transferability across domains and continuous state spaces
Dynamic mechanical behavior of frozen Ottawa sand subjected to high strain rate loading
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01The student, Cocou Davis Ruben Aza-Gnandji, accepted the attached license on 2025-07-14 at 16:04.The student, Cocou Davis Ruben Aza-Gnandji, submitted this Dissertation for approval on 2025-07-14 at 16:14.This Dissertation was approved for publication on 2025-07-17 at 06:39.DSpace SAF Submission Ingestion Package generated from Vireo submission #22553 on 2025-10-21 at 10:06:05Increasing temperatures in the Arctic region raise environmental concerns, but also bring new opportunities for civil engineers including infrastructure development, such as roads, buildings, and pipelines, etc. Extensive research focusing on the behavior of frozen soils under quasi-static loading resulting in small strain rates exist; however, limited studies have explored their behavior under high strain rate loading, which is relevant in construction and resource extraction, etc. This research aims to investigate the dynamic mechanical behavior of frozen sands under varying thermal conditions and to characterize the impact of high strain rates on the overall stress-strain response. Frozen Ottawa sand samples having different dry densities and initial degrees of saturation were tested at temperatures of -15, -10, and -5°C to characterize the mechanical behavior. These samples were subjected to strain rates ranging from 400 to 1500/s using both traditional and modified Split Hopkinson Pressure Bar (SHPB). A temperature-controlled chamber was designed and attached to the SHPB setup to maintain constant temperatures during the experiments. A high-speed infrared camera was integrated to monitor temperature variations during the impact tests for estimating the thermal energy during the tests. The stress-strain curves of frozen Ottawa sand at different temperatures were obtained, and the results indicated that the stress-strain behavior was significantly influenced by the strain rate, temperature, and initial degree of saturation of the frozen sands. Specifically, the stress-strain curves exhibited peak stresses followed by pronounced strain softening when the strain rate was less than 900/s. However, at strain rates above 900/s, relatively more brittle response was observed. The results also revealed that the strength of the frozen Ottawa sand increased as the temperature decreased, due to the enhanced bonding between the ice and soil particles. To further evaluate the behavior of frozen sands under various strain rates, numerical simulations using LS-DYNA were performed. Two numerical methods available in LS-DYNA, namely, the Finite Element Methods and the Smoothed Particle Hydrodynamics, were employed to perform the numerical simulations of the SHPB tests. Holmquist-Johnson-Cook material model was employed in the simulations. Both numerical schemes produced results that were in good agreement with the experimental results. They also revealed the need for the development of advanced material models for the simulations of the dynamic behavior of frozen soils under extreme loading conditions. Key experimental results of this study can contribute to the design of infrastructure and protective structures that may be subjected to high-strain-rate deformations, impact loadings, or explosions. Future research will build on this study to develop a material model with temperature-dependent parameters for simulating the thermo-mechanical behavior of frozen sands under different thermal and extreme loading conditions