University of Illinois at Chicago
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IN THE SUPREME COURT OF CALIFORNIA FAMILY VIOLENCE APPELLATE PROJECT and BAY AREA LEGAL AID, Petitioners, v. SUPERIOR COURT OF CALIFORNIA, COUNTIES OF CONTRA COSTA, LOS ANGELES, SANTA CLARA, and SAN DIEGO, Respondents. APPLICATION FOR LEAVE TO FILE AMICUS CURIAE BRIEF IN SUPPORT OF PETITIONERS; AMICUS CURIAE BRIEF
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Modular Differential-Mode Solid-State Transformer
No full textSolid-State Transformer (SST) technology is a promising alternative to conventional low-frequency transformers (LFTs), addressing key limitations in modern power systems. Traditionally, LFTs have been utilized for interconnecting regional grid networks, enabling efficient long-distance power transmission. However, their bulky and heavy monolithic structure poses challenges in transportation, replacement, and upgrading, which impedes the rapid modernization of grid infrastructure. In contrast, SST technology employs high frequency (HF) electric voltage is imposed across a transformer by leveraging power electronics to reduce the size of the overall system.
This thesis presents a modular differential model (DM) SST technology based on single-stage differential mode ac/ac converter (DMAC). The proposed solution offers modular voltage and power scalability while ensuring high efficiency. A novel modulation scheme for the proposed DM-SST module allows high efficiency operation for wide operating range by ensuring zero voltage switching (ZVS) turn-on on all switches resulting in significant loss reduction in the converter. The current source nature of the input and output port of the DM-SST module greatly simplifies control for modular scaling, making the technology suitable for medium voltage systems.
The DM-SST module is validated using a 1 kVA 220 V rated experimental prototype with peak efficiency of 97.6%. The proposed DM-SST is validated to offer wide output voltage regulation, reactive load handling capability, and reactive power compensation capability without the presence of DC-link capacitor, further enhancing the real-world application. The DM-SST system based on 3x2 module configuration is also implemented to demonstrate equal voltage balancing between the modules
AI-Bind 2.0: Leveraging Pre-trained Language Model for Protein-Ligand Binding Prediction
No full textIn recent years, machine learning and deep learning have significantly reshaped the landscape of computational biology and drug discovery, enabling data-driven insights into complex biological interactions. This thesis presents AI-Bind 2.0, an innovative computational pipeline designed to enhance protein-ligand binding predictions with higher accuracy and robustness compared to previous models. The core advancement of AI-Bind 2.0 lies in its integration of pre-trained protein language models, specifically the ProtTrans model, a transformer-based architecture trained on vast biological datasets. By leveraging ProtTrans, AI-Bind 2.0 can capture intricate dependencies within protein sequences, yielding more precise, contextually rich representations of protein-ligand interactions than those achieved with earlier embedding techniques, such as ProtVec. To validate its effectiveness, AI-Bind 2.0 was trained and tested on a rigorously curated dataset drawn from sources like DrugBank, BindingDB, and Drug Target Commons, encompassing a balanced mix of binding and non-binding interactions. The evaluation was conducted using both transductive and inductive testing approaches to measure the model’s generalization capabilities. Performance metrics, including the Area Under the Receiver Operating Characteristic Curve and the Area Under the Precision-Recall Curve, demonstrate AI-Bind 2.0’s notable improvements over its predecessor, particularly in handling interactions involving novel proteins and ligands. Additionally, AI-Bind 2.0’s application in COVID-19 research highlights its potential to expedite drug discovery by accurately predicting binding interactions with key viral proteins, thus aiding in the identification of promising therapeutic candidates. The contributions of this research underscore the potential of advanced natural language processing techniques in bioinformatics, demonstrating that transformer-based models can effectively capture complex biological patterns critical to drug discovery. By establishing a more accurate and generalizable framework for predicting protein-ligand interactions, AI-Bind 2.0 offers a valuable tool for computational biology and opens new avenues for research in understanding and targeting molecular mechanisms
Exploring Health Learners' Perceptions of AI Integration in the Curriculum-A Survey Tool and Findings
No full textBackground
Few studies have assessed students in different healthcare disciplines’ perceptions of the importance of learning standard competencies in AI technology. This study aimed to evaluate the learning needs, apprehensions/anxieties, and digital self-efficacy of students in medicine, nursing, and pharmacy regarding AI technology in healthcare.
Methods:
This cross-sectional survey study included 521 students: 384 medical, 67 nursing, and 78 pharmacy students, with response rates of 32%, 61%, and 52% from the colleges of medicine, nursing, and pharmacy, respectively. Survey data were collected over a 3-month period using a self-reported questionnaire. Data analysis was conducted using SPSS, including descriptive statistics, ANOVA, and chi-square tests.
Results:
Students from all three disciplines agreed on the importance of learning about AI in healthcare, with agreement rates for the six core competency domains ranging from 80 to 92%. The AI Learning Anxiety/Fears questionnaire revealed varying anxiety levels about AI technology learning across the disciplines. Over 20% of students across all disciplines agreed or strongly agreed that they experience anxiety on the AI Learning Anxiety/Fears questionnaire. The digital self-efficacy scale score was negatively correlated with AI learning anxiety (r=-0.32, p=0.01).
Conclusion:
This study offers insights into students' perspectives on learning about AI, which will inform the development of effective AI curricula. These findings highlight the need for standardized AI curricula that can address learning needs and reduce apprehensions across healthcare disciplines
Validity Evidence for the Use of a Discharge Observation Tool
No full textPurpose
Patients' lack of understanding of treatment plans can result in readmissions and adverse events after discharge from the hospital. This study examines validity evidence supporting the use of a workplace-based direct observation tool and aims to align assessment of medical students’ ability to provide discharge instructions with patient-reported outcomes.
Method
To assess content validity, faculty and residents reviewed items of the Discharge Observation Tool (DOT) for relevance to discharging patients. After rater training, study participants viewed two videos of simulated discharge instructions and scored student performances using the DOT. Response process was assessed by determining the consistency and accuracy of ratings; DOT psychometric characteristics were evaluated for internal structure validity. To examine the alignment between DOT and patient-reported understanding of discharge instructions, logistic regression was used to determine if mean score on DOT could predict patient responses in post discharge phone calls.
Results
The DOT was rated as being relevant to patient discharge. The overall rater-consistency agreement measured using intraclass correlation (ICC) was 0.80; rater accuracy between trained raters and experts was 0.72. Learner variance was 11%. Phi-coefficient reliability was .70, demonstrating acceptable reproducibility for formative assessments. Higher DOT scores were associated with a higher likelihood of patient-reported knowledge of what to do in urgent or emergent situations (OR = 29.52, P = 0.002) and a lower likelihood of patient report of having barriers for attendance of follow up appointments. (OR = 6.08, P = 0.006).
Conclusions
The results of this study suggest that there is adequate evidence of content relevance, response process and reliability for the use of this workplace-based assessment as a formative tool. Evidence also suggests alignment in the assessment of student’s ability to provide discharge instructions with patient reported outcomes of knowledge of red flags and of barriers to attending follow up appointments
Precision Assessment and Intervention of Tongue Movement Following Neurological Injury
No full textThe Tongue-Trackpad is a wireless intraoral device developed to assess and rehabilitate
tongue movement impairments caused by neurological injuries, such as strokes or traumatic
brain injuries. Initially, the device demonstrated its potential as a diagnostic tool by quantifying tongue movement deficits, establishing a foundation for clinical applications. Subsequent
interface improvements introduced dynamic grayscale feedback, which enhanced user interaction during rehabilitation tasks. The third chapter of the research documented the use of the
Tongue-Trackpad in therapeutic interventions, where personalized protocols were applied in pilot clinical trials, showing measurable improvements in tongue range of motion and reductions
in excessive contact areas. The fourth chapter focused on broader clinical applications, evaluating the device’s use in a multi-participant pilot study, assessing its practicality and effectiveness
in clinical rehabilitation. These findings suggest that the Tongue-Trackpad has the potential
to bridge the gap between objective diagnostic tools and personalized therapeutic approaches
for tongue motor impairment
Reactive Thiol Frameworks Enabled by Peptide Assembly
No full textThiols are potent nucleophiles, redox-active, and capable of undergoing a variety of reversible reactions, making their incorporation into porous materials such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) highly desirable. However, their high reactivity and sensitivity have posed significant challenges for direct incorporation into these frameworks, relying on postsynthetic modifications to graft thiol units randomly or incompletely throughout the materials. The less-defined nature of MOFs and COFs has prevented thorough structural characterization, especially by single-crystal X-ray diffraction (SC-XRD) impeding the rational engineering of their function. We address this challenge by employing a noncovalent peptide-π-stack assembly strategy which uses shape-specific, weak interactions including van der Waals, H-bonding, and π−π interactions encoded by a peptide sequence to control the framework synthesis. Hence, our approach tolerates the presence of reactive thiol groups during the assembly process to provide a novel and convenient route toward well-defined thiol-containing materials. Chapter 1 outlines the importance of noncovalent interactions, specifically π-stacking, in affecting the self-assembly process of peptide-based materials. Their significance in determining the structural and functional properties of the resulting nanostructures through strong, directional, and predictable interactions is highlighted. The chapter further explores how the specific site of attachment of these π-stacking units to the peptide backbone influences the type of nanostructures formed —including spheres, fibers, and tubes— each with distinct physical properties and potential applications. The relationship between molecular design and self-assembled nanomaterial further highlights the versatility and precision of aromatic units within a peptide-based self-assembly system. Chapter 2 focuses on the exceptional versatility of thiol groups. Leveraging the mild conditions of our noncovalent peptide assembly, we readily synthesized and characterized a number of frameworks with thiols displayed at many unique positions and in several permutations via SC-XRD. Furthermore, the thiol-containing frameworks undergo diverse single-crystal-to-single-crystal reactions, including toxic metal ion coordination (e.g., Cd2+, Pb2+, and Hg2+), selective uptake of Hg2+ ions, and redox transformations. Chapter 3 explores the formation of dichalcogenide bonds, specifically sulfenic acid, persulfide, thioselenide, and thiotelluride bonds. Enhancements to my former noncovalent framework through the addition of a β-sheet and extending the α-helix length increased stability and pore sizes. Through iodine pre-functionalization and subsequent reaction with hydrochalcogen sodium salts, the formation of RSOH, RSS¯̄, RSSe¯̄, and RSTe¯̄ bonds was confirmed via SC-XRD. Additionally, reactivity studies were performed through reactions with 5,5-Dimethyl-1-Pyrroline-N-Oxide serving as evidence of thiyl radical formation. In conclusion, this thesis overcomes the long-standing challenge of incorporating thiols into porous crystalline materials through rational design. It highlights the versatility of thiols in stabilized environments, demonstrating applications as molecular sieves for toxic heavy metals and precursors to functional groups such as sulfonic acids and nitroso thiols. Additionally, we elucidate the formation of labile dichalcogenide bonds and thiyl radicals, underscoring the potential of thiol-functionalized frameworks for advanced applications
Addressing Early Onset Colorectal Cancer Risk Factors Through Weight Management and Stress Reduction
No full textEarly-onset colorectal cancer (EOCRC) is defined as a diagnosis of colorectal cancer in patients younger than 50 years old. The American Cancer Society reported that 12% of all colorectal cancer (CRC) diagnoses occur in individuals younger than 50 years old. Worldwide, a steady increase in EOCRC cases is observed among Westernized countries, suggesting that similar risk factors and exposures within these developed countries contribute to EOCRC incidence. Increased adiposity from an early age that persists through adulthood, poor diet quality and chronic psychosocial stress (CPS) are under investigation as drivers of the recent uptick in EOCRC in the United States and other Westernized countries. This dissertation research examined diet quality, chronic stress and body weight as potential modifiable risk factors for EOCRC in young adults.
The following is presented in this dissertation: first, a manuscript with a review of diet quality, excess adiposity, and CPS and their implications for EOCRC risk. Second, a manuscript of a study design protocol describing an 8-week pilot randomized controlled trial investigating the feasibility and acceptability of time restricted eating (TRE) for weight management and mindfulness for stress reduction as interventions to risk factors associated with EOCRC is presented. Lastly, a manuscript stating the outcomes of the pilot study is presented.
This dissertation research has shed light on the current evidence highlighting association between body weight, dietary quality and CPS and EOCRC risk in developed countries. We also completed a randomized controlled pilot study demonstrating the feasibility and acceptability of a remote TRE and mindfulness intervention among young adults with obesity and moderate to higher self-reported CPS. This study was feasible and well-accepted among its participants. The TRE & Mindfulness arm demonstrated superior improvements in perceived stress, visceral fat mass, fasting insulin, fasting glucose, homeostasis model assessment, hemoglobin A1c, and fecal calprotectin than the other three study arms (TRE alone, Mindfulness alone, and Control). The feasibility and preliminary data generated here will now serve in developing a fully powered efficacy trial of TRE and mindfulness to address risk factors associated with EOCRC among young adults
Resilient and Sustainable Biogeochemical Landfill Cover for Mitigating Fugitive Gas Emissions
No full textMunicipal solid waste (MSW) landfills are a significant source of greenhouse gas emissions, contributing to global climate change. Conventional landfill gas (LFG) management systems, such as gas collection and soil cover (SC) systems, are limited in their ability to fully capture and mitigate these emissions. This research explores the development of an innovative biogeochemical cover (BGCC) system designed to enhance the mitigation of methane (CH₄) and carbon dioxide (CO₂) through microbial oxidation and mineral carbonation processes, respectively. The proposed BGCC system integrates biochar-based biocovers for CH₄ oxidation and alkaline industrial byproducts for CO₂ sequestration, offering a more comprehensive approach to gas mitigation compared to traditional SC systems.
The study initially focused on near-field-scale testing of a BGCC system incorporating pinewood biochar based biocover and basic oxygen furnace (BOF) slag, comparing it to conventional SC systems. The BGCC system demonstrated higher CH₄ removal efficiency (74.7–79.7%) at moderate influx rates (23.9-25.5 g CH₄/m²·day) and achieved complete H₂S removal, while also sequestering CO₂ through mineral carbonation. However, CO₂ breakthrough occurred after 156 days of continuous exposure, likely due to desiccation of the BOF slag layer. Microbial analysis revealed that Type I methanotrophs, such as Methylobacter, dominated the BGCC systems, driving CH₄ oxidation.
To further enhance the BGCC system’s performance and reduce reliance on BOF slag and pinewood biochar, alternative materials were investigated. Batch experiments identified cement kiln dust (CKD) as a promising replacement for BOF slag due to its higher CO₂ sequestration capacity (225 mg/g compared to 98.1 mg/g for BOF slag). Similarly, rice husk (RH) biochar exhibited the highest CH₄ oxidation potential (6595.4 µg CH₄/g) among six biochars produced from different feedstocks, including pinewood biochar.
Following the evaluation of these alternative materials, a modified BGCC system incorporating RH biochar and CKD (BGCC-RHCKD) was developed and compared to the previously developed BGCC-PWBOF (pinewood biochar and BOF slag) and SC systems. The BGCC-RHCKD system achieved 100% CO₂ removal efficiency without breakthrough, outperforming BGCC-PWBOF, while maintaining CH₄ removal efficiency comparable to that of the SC system at moderate influx rates (20.77-22.80 g CH₄/m²·day) during Phase 1. Microbial analysis revealed that Type I methanotrophs, such as Methylobacter luteus, dominated the BGCC systems (BGCC-RHCKD and BGCC-PWBOF), driving CH₄ oxidation.
Overall, this study demonstrates that BGCC systems offer a comprehensive solution for LFG management by effectively mitigating CH₄, CO₂, and H₂S emissions while promoting the circular economy through the valorization of waste materials. The adaptability of BGCC systems to variable gas compositions and influx rates, coupled with their dual capability for emission reduction and resource recovery, positions them as a sustainable and resilient alternative to conventional LFG management practices
From Collections to Dialogues: The Role of Language and Queer Theory in Transforming Museums
No full textEvery museum you have ever entered, every exhibit you have wandered through, every
artifact you have admired has been carefully curated–filtered through systems of power that
shape what you see and, more importantly, what you do not. These cultural institutions, long
regarded as centers of knowledge, are far from impartial. Museums serve as much more than just
cultural vaults or historical platforms–they are dynamic storytellers that bring history to life.
Through that storytelling, museums actively construct history, which in turn, informs
communication and dissemination of knowledge structures. Moreover, it is through this
construction that museums tend to erase, distort, and misrepresent the history and cultures of
those who do not fit in the dominant narrative. What if everything you thought you knew about
museums was just another story–a carefully crafted narrative designed to conceal as much as it
reveals?
This essay works to disrupt these misconceptions of museums as arbiters of truth, by
critically examining the foundational structures of museums and the ways in which power and
privilege shape what is preserved, displayed, and excluded. The scope of this project covers
European and North American museums that display culture, with a particular focus on museum
roles that encompass public engagement. I pause here to recognize the multifaceted nature of the
museum, and the many avenues available for inquiry–one being the research branch of these
institutions. In being centers of knowledge, museums produce knowledge through innovative
scientific research breakthroughs as well as exhibition display narratives. This thesis focuses
solely on the public engagement aspects of museal spaces (across many different genres of
museums: natural history, cultural, art, scientific, children’s, historical, etc.) that inform,
perpetuate, and produce knowledge systems.
By going further than just working with critical museological methods, this essay will
work with the principles of queer theory–namely, disrupting structures of power, knowledge and
control; challenging norms and unsettling fixed meanings; and embracing fluidity as a means of
exploring potentiality–offering alternative frameworks for understanding how museums produce
knowledge and display culture. It explores how these structures influence contemporary
linguistic trends within museums, underscoring the significance of language–and its delivery
through narrative and storytelling–in shaping historical understanding
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