University of Illinois at Chicago
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Metallized Diamond Photocathode Data
No full textRaw data used to characterize a metallized diamond photocathode; .tif files obtained from the experimental system which may be analyzed to determine spectral MTE and QE characteristics for this photocathode.</p
Student Success Assessment Tools Workshop Series
No full textThe full series can be found on YouTube: Student Success Assessment Tools Workshop SeriesWorkshop Overview and FundingThe Student Success Assessment Tools Workshop Series was designed to equip librarians with practical competencies for using assessment tools to measure and enhance library contributions to student success. During Summer 2025, the workshop series engaged around 100 selected participants from 50 institutions, offering practical experience in assessment and data use. This workshop series is part of the two-year project “Implementing Assessment Tools for Library Impact on Students’ Academic Success” (2024–2026), supported in part by the Institute of Museum and Library Services (LG-256587-OLS-24). The project builds on an earlier IMLS grant (LG-252338-OLS-22) that developed assessment tools and now provides workshops and consultations to help academic libraries put these tools into practice.Workshop Syllabus and ContentsSyllabusTraining workshops cover all aspects of using assessment tools, including:Submitting Institutional Review Board (IRB) applicationsNavigating assessment tools to measure student successAnalyzing assessment data to measure library impact on student successParticipants learn to interpret findings and translate them into actionable insights that support continuous improvement in library services and programs.Workshop Series Recordings:The Student Success Assessment Tools Workshop Series includes five sessions designed to guide participants through each stage of the assessment process—from understanding IRB procedures to communicating results. The full series can be found on YouTube: Student Success Assessment Tools Workshop SeriesPreworkshop: Overview of Student Success Assessment Workshop SeriesIntroduce the goals of the funded research projectWalk through assessment toolsOverview the workshop seriesWorkshop 1: Demystifying IRB approvalUnderstand IRB proceduresUnderstand the approval process in research settingsWorkshop 2: Navigating assessment tools to measure student successExplore the assessment toolsDelve into the details of the survey questionsIntroduce strategies for survey distributionWorkshop 3: Beyond the numbers- A dynamic dive into quantitative and qualitative dataIntroduction to types of data analysis for the assessment toolWorkshop 4: Developing a reportRead example template reportsGenerate reports with the data analysis techniquesCreate reportsWorkshop 5: Communicating the results and implementationDiscussions on strategically sharing survey resultsRead example template reportsDetermine the targeted audienceWorkshop Quizzes:Quizzes from the workshops can be found at:Workshop Quizzes</p
Discovering Fat Studies Literature: A Bibliometric Analysis
No full textThis study engages with information literacy with respect to the marginalizing experience of fat oppression. The aim is to illuminate assumptions underlying research and scholarship into the lives of people living in larger bodies and to investigate how researchers can discover studies founded on either a medicalization or fat studies paradigm. A bibliometric analysis reveals where fat studies literature is published and indexed, and how key words differ between authors and indexes. The study is important not only for courses in fat studies, but also as a valuable exemplar for critical thinking in information literacy instruction across disciplines.</p
A Novel Approach to Movement Profiling: Multi-Task Classification for Enhanced Orthopedics Assessment
No full textHuman movement analysis is integral for diagnosing, managing, and treating orthopedic pathologies. Traditional marker-based Motion Capture (MoCap) systems have long been considered the gold standard for capturing kinematic data. However, they face several limitations in clinical scalability due to cost, complexity of setup procedures, and restricted ecological validity. These challenges have driven interest in markerless systems, which leverage advancements in computer vision to provide a scalable and cost-effective alternative. Despite their potential, they require rigorous validation to ensure their accuracy and reliability for clinical use. This study investigates the use of clustering techniques to identify movement-based subgroups in patients while evaluating the consistency between systems.
To achieve this, marker-based and markerless kinematic data were simultaneously collected from participants during a series of functional tasks, chosen for their clinical relevance in assessing lower-limb biomechanics. Preprocessing steps, including noise filtering, temporal resampling, and gap-filling, were applied to the raw data to ensure consistency and comparability across systems. The resulting standardized datasets formed the basis for an analysis pipeline
designed to extract meaningful insights from the data, which consisted of four key stages:
1. Dynamic Time Warping (DTW) was employed to quantify temporal alignment between marker-based and markerless MoCap systems;
2. Biomechanical features were selected based on thresholds derived from system agreement results and their clinical relevance;
3. K-means clustering was applied to group participants based on their task-specific kinematic profiles, revealing patterns within individual tasks;
4. Hierarchical clustering was subsequently used to identify broader movement subgroups across multiple tasks.
Preliminary results showed a high level of agreement between marker-based and markerless systems for kinematic measurements, especially in the frontal and sagittal planes. In contrast, the reliability of the transverse plane was notably lower, highlighting an area for improvement in markerless systems. Even if markerless MoCap systems have the potential to revolutionize orthopedic diagnostics, further research is needed to address the identified limitations.
The clustering analysis was conducted using two methods: one based on relevant data points from the kinematic curves, and the other considering the whole time series. The results revealed distinct subgroups within the cohorts, underscoring the presence of biomechanical heterogeneity and providing valuable insights into movement patterns that could inform personalized rehabilitation strategies. The validation of those findings will be essential to ensure their generalizability and clinical utility.
In conclusion, this study demonstrates the feasibility of integrating markerless MoCap systems with clustering techniques to complement traditional methods in orthopedic diagnostics. By providing a scalable and personalized approach to movement analysis, these systems have the potential to enhance clinical decision-making and improve patient outcomes
Leveraging Process Mining and Deep Learning to Improve Health and Safety Outcome Predictive Models
No full textThe ability to predict future events is a valuable asset across many sectors, enhancing decision-making, risk management, and system efficiency. This thesis investigates the application of deep learning in predictive modeling across three key domains: process optimization, healthcare, and occupational safety. The first contribution introduces the Adjacency Matrix Deep Learning Prediction (AXDP) Model, which combines graph theory and deep learning to forecast sequential events. AXDP demonstrated superior performance on eight public datasets, outperforming existing models in prediction accuracy. The second contribution presents a deep learning model to predict mortality in ICU patients with Paralytic Ileus, using ablation and SHAP analyses to identify critical clinical predictors. The third contribution focuses on predicting neurologic outcomes in out-of-hospital cardiac arrest (OHCA) patients, showing that early clinical data can effectively forecast outcomes and that novel biomarkers may further improve accuracy. The final contribution explores whether occupational safety and health (OSH) professionals are prepared to integrate AI into workplace safety efforts. To address this, the Artificial Intelligence Learning Framework for Occupational Safety Professionals (ALFO) was developed and evaluated. In two studies involving 114 participants, ALFO significantly improved AI literacy, with 80% of participants recognizing AI’s potential to enhance safety. Together, these contributions highlight the impact of predictive deep learning and targeted education in advancing safety, efficiency, and healthcare outcomes
Chemical Biology Tools for Biochemical and Functional Analysis of Myosin Phosphatase
No full textAtrial fibrillation (AF) is a common cardiac arrhythmia characterized by irregular electrical impulses and impaired atrial contraction. This mechanical dysfunction contributes to blood stasis and increases the risk of thrombus formation, even after restoration of normal rhythm. Emerging evidence links atrial hypocontractility in AF to dysregulation of myosin light chain phosphatase
(MLCP), a holoenzyme composed of a catalytic subunit (PP1cβ) and a regulatory subunit that targets the phosphatase to sarcomeric substrates. PPP1R12C is an atrial-enriched MLCP regulatory subunit that directs PP1cβ to the myosin regulatory light chain (MLC2a), modulating contractile force. In AF, PPP1R12C is upregulated, leading to excessive dephosphorylation of MLC2a and weakening of atrial contraction. This dissertation addresses the structural and functional analysis of the PPP1R12C–PP1cβ holoenzyme using a suite of chemical biology tools. To overcome challenges posed by PPP1R12C’s intrinsic disorder and the dynamic assembly of the complex, we employed mammalian expression systems and AlphaFold modeling to predict structure and optimize expression. A ligand-inducible degron system was developed by fusing PPP1R12C to a destabilized eDHFR domain, allowing rapid, reversible stabilization of the protein with
trimethoprim (TMP). This approach enabled tight temporal control of PPP1R12C levels in HL-1 atrial cardiomyocytes. Functional assays demonstrated that stabilization of PPP1R12C reduced MLC2a phosphorylation and contractile amplitude in atrial cells, mimicking the hypocontractile phenotype observed in
AF. Conversely, PPP1R12C depletion restored phosphorylation levels and improved contractile function. These findings establish a direct causal link between PPP1R12C abundance and atrial contractility and underscore the critical role of myosin phosphatase regulation in sarcomeric signaling. In summary, this work provides new biochemical and functional insights into the PPP1R12C–
PP1cβ holoenzyme and introduces versatile experimental tools to dissect its regulation. These findings support the therapeutic potential of targeting myosin phosphatase in atrial-specific cardiac disorders such as atrial fibrillation
Fiducial Inference for Mixed-Effects Models: A Frequentist Advancement for Small-Sample Problems
No full textThis dissertation develops a comprehensive fiducial inference framework for mixed-effects models, offering a robust frequentist alternative for statistical inference in small-sample settings. Traditional methods such as maximum likelihood estimation (MLE), Wald-type intervals, and bootstrap techniques often fail to provide accurate interval estimates when sample sizes are limited—a common scenario in epidemiologic studies, rare disease trials, and environmental monitoring. To address these limitations, this work builds on Fisher’s original fiducial argument and its modern extensions, proposing a structural-equation-based approach that avoids reliance on large-sample approximations and prior distributions.
The proposed methodology is applied across three key domains. First, in agreement analysis using generalized linear mixed models (GLMMs), fiducial confidence intervals are developed for the concordance correlation coefficient (CCC), a widely used measure of reliability. This is particularly relevant in evaluating the consistency of AI-assisted medical diagnostics with human experts. Second, in the context of environmental and public health, fiducial methods are applied to random-effects calibration models, enabling precise interval estimation of unknown chemical concentrations across multiple laboratories. This approach accounts for both additive and multiplicative sources of measurement error, improving analytical precision in regulatory and exposure assessment settings. Third, an exact fiducial method is introduced for small area estimation (SAE) using linear mixed-effects models, allowing for accurate prediction intervals for domain-specific parameters without relying on asymptotic theory or computationally intensive resampling.
Extensive simulation studies demonstrate that the proposed fiducial methods consistently achieve nominal coverage with narrower confidence intervals compared to classical approaches. The methods are shown to be robust across balanced and unbalanced designs, Gaussian and non-Gaussian responses, and both linear and non-linear mixed-effects models. Real-world applications—including interlaboratory studies of cadmium and copper concentrations—further validate the practical utility and computational efficiency of the fiducial framework.
This research contributes a unified, scalable fiducial inference methodology that enhances analytical rigor in small-sample scenarios. It offers a valuable toolkit for statisticians and applied researchers working in biostatistics, environmental science, and public health. Future directions include extending the framework to high-dimensional data, deeper hierarchical models, and multiple testing problems
Exploring Trauma and Its Impact on Disabled Students: An Analysis Using the Future of Families Study
No full textThe American Psychological Association (APA) (n.d.) defines trauma as an emotional reaction to a terrible life event like rape, violence, or natural disaster. When trauma is experienced by a child on a repetitive and pervasive basis, it is referred to as complex trauma (van der Kolk, 2005). Felitti et al. (1998) coined the term adverse childhood experience (ACEs) in their study exploring the impact of adverse events on later life health outcomes. In the United States, nearly two thirds of adults have experienced at least one ACE and nearly one fifth have experienced four or more ACEs (Swedo et al., 2023). In the education setting, exposure to ACEs can lead to decreased educational attainment and engagement. However, little is known about the educational outcomes or interventions for students with disabilities. This dissertation explores the current intervention strategies for students with disabilities in education who have experienced trauma, the impact of ACE exposure on education outcomes for students with disabilities compared to their non-disabled peers, and critiques the methodology used to study ACEs
Declarative Analytics on Heterogeneous HPC Systems
No full textThe emergence of exascale systems marks a transforming era in high-performance computing (HPC) powered by extensive use of GPUs. GPGPU's popularity in HPC, due to performance gains and power efficiency, demands redesigning traditional algorithms to exploit GPU parallelism. However, declarative languages, like Datalog, can directly leverage these advancements due to their ability to express complex problems through simple rules and queries, which can be efficiently compiled into relational algebra operations for execution on GPGPUs. Integrating Datalog's declarative syntax with GPGPU's computational power enables scalable declarative analytics across big data, graph mining, and program analysis on HPC systems.
While recent advancements have focused on multi-threaded and multi-core implementations of Datalog, the evolution of exascale systems presents a compelling opportunity to extend Datalog’s capabilities to multi-node, multi-GPU environments. This thesis addresses this gap by developing the first multi-GPU, multi-node Datalog engine. First we investigate the parallelization of iterated operations involving relational algebra primitives on GPUs, which are fundamental to Datalog operations. Then, we address challenges specific to heterogeneous architectures, including optimized communication strategies, recursive aggregation techniques, and efficient join operations, all tailored for a heterogeneous Datalog backend. We focus on optimizing specialized Datalog implementations for graph algorithms, including path-finding and topology-based feature extraction. For testing and benchmarking of the algorithms, we utilize publicly available datasets from the Stanford Large Network Dataset Collection and the SuiteSparse Matrix Collection. Our research extends beyond traditional graph mining and program analysis, exploring Datalog's potential in emerging domains such as topological data analysis, machine learning, and visual analytics for high-dimensional data. Evaluating power consumption alongside performance enhancement is increasingly vital in HPC systems, as energy efficiency significantly impacts operational sustainability and cost-effectiveness. Thus we conduct power analysis across GPU-based Datalog engines, which differ primarily in their recursive join strategies and underlying data structures. We evaluate how variations in implementation techniques for the same application, executed on identical hardware and datasets, influence power consumption. By advancing Datalog's applicability in exascale environments, we aim to demonstrate its scalability and suitability for performance and energy-efficient analysis of complex data on next-generation computing platforms
How Food Insecurity Impacts Children’s Oral Health Outcomes
No full textObjectives: The objectives were to understand how food security status impacts children receiving dental care at the University of Illinois Chicago College of Dentistry (UIC COD) and to explore associations between food security status and a pediatric patient’s caries status.
Methods: A questionnaire was distributed to the legal guardians of children ages three to seventeen years during their dental examinations. The child’s caries status and tentative treatment plan were abstracted from their electronic dental records. Descriptive statistics and bivariate analysis (Chi-Square test) were performed using SPSS with a significance threshold set at P<0.05.
Results: One Hundred and Twenty-Seven children with a mean age of 7.28 years (SD 3.11) at the time of their examination were recruited for the study. Sixty-three percent of children were female. Thirty-six percent of children identified as White, 24% Black, 4% Asian, 3% American Indian/Alaskan Native, 30% Other, and 2% Did Not Report. Fifty-eight percent identified as Hispanic or Latino. Three (2.4%) households reported experiencing marginal food security, 71 (55.9%) low food security, and 53 (41.7%) very low food security. According to the DMFT Scale – 25 patients (19.7%) had Low Severity DMFT (0-5), 62 (48.8%) had Moderate Severity DMFT (6-10), 31 (24.4%) had High Severity DMFT (11-15) and 9 (7.1%) had Extreme Severity DMFT (16+). The Chi-square analysis revealed no significant association between the food security status and DMFT scores (P=0.453).
Conclusion: With no significant association present, research has shown a potential mechanism linking food insecurity and tooth decay due to dietary behaviors
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