University of Illinois at Chicago
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Orbits in Tree Varieties
No full textIn this paper, we introduce tree varieties as a natural generalization of products of partial flag varieties. We study orbits of the action on tree varieties. We characterize tree varieties with finitely many orbits, generalizing a celebrated theorem of Magyar, Weyman and Zelevinsky. We give criteria that guarantee that a tree variety has a dense orbit and provide many examples of tree varieties that do not have dense orbits. We show that a triple of two-step flag varieties has a dense orbit if and only if .</p
Exploring the state of evidence on aging with HIV in long-term care: A scoping review protocol
No full textBackgroundThe growing population of older adults living with HIV presents unique challenges for long-term care facilities, which are increasingly tasked with supporting residents who require both HIV-specific and geriatric care. Despite advances in HIV treatment that have extended life expectancy, the needs of these individuals in long-term care remain underexplored, and the field lacks a consolidated understanding of how facilities are currently equipped to manage these complexities. This scoping review protocol outlines the approach for synthesizing existing evidence on the experiences, challenges, and care outcomes of aging with HIV in long-term care settings.ObjectiveTo examine the state of evidence on older adults with HIV in long-term care, providing an overview of current knowledge on the health, social, and systemic factors influencing their care and identifying gaps that may guide future research and practice.MethodsThe team includes knowledge users, including experts by experience, to ensure the findings are grounded in lived realities and practical applicability. Using a scoping review framework by the Joanna Briggs Institute, we will conduct a comprehensive search of literature from inception in the following electronic databases: MEDLINE (R) ALL (Ovid), Embase Classic + Embase (Ovid), Cochrane Central Register of Controlled Trials (Ovid), CINAHL Ultimate (EBSCO), PsycINFO (Ovid), AgeLine (EBSCO), and Scopus to capture studies that address aging with HIV in long-term care settings. Eligible studies will be screened and selected based on criteria focused on relevance to the intersection of aging, HIV, and long-term care. Articles will be screened by two reviewers. Data will be charted and synthesized thematically, allowing for an organized summary of findings on key topics such as physical and mental health, care provision, and facility preparedness.Discussion and implicationsThis review will offer an overview of the current state of knowledge on aging with HIV in long-term care facilities, highlighting what is known about care practices, health outcomes, and systemic challenges in these settings. Findings will clarify the breadth and depth of existing evidence and reveal areas requiring further research, thereby informing policy and enhancing care strategies for this population.</p
<i>Bacteriophages as an Alternative to Antibiotics</i>
No full textTitle: Bacteriophages as an Alternative to Antibiotics Company/Institution: University of Illinois Chicago, Biomedical Visualization Medium/Software: Animated in Cinema4D, materials created using Redshift, post compositing and audio in Aftereffects. Final Presentation Format: Mp4 Video Primary Audience: Academic audienceIntended Purpose: This animation was designed for a general academic audience to introduce bacteriophages as a potential alternative to antibiotics for treating infectious diseases. The story follows a bacteriophage as it targets an antibiotic-resistant bacterium within a cellular environment of intestinal villi. To illustrate the bacterium's resistance, its color changes as it becomes unaffected by antibiotics. While therapeutic treatments using bacteriophages are still undergoing clinical trials, they have shown promising results. This animation provides a simple overview of the mechanism by which bacteriophages target and infect bacteria.</p
Promoting Health Literacy and Cultural Humility: CBOs and Wraparound Services
No full textAbstract: The COVID-19 pandemic placed demands on community-based organizations (CBOs) to address human needs to promote the health and well-being of diverse communities experiencing high rates of disparities. To enhance the capacity of CBOs in engaging with their communities, we developed webinars on health literacy and cultural humility. The concept that drove the training was wraparound services, with the objective to increase CBOs’ skills and knowledge for addressing the needs of the whole person.</p
Helpers in Learning Systems
No full textRecent advancements in AI have reshaped how AI agents interact with other agents, transitioning beyond static, repetitive processes to adaptive, multiagent collaborations. With the growing importance of AI agents in the era of Large Language Models (LLMs), we present two distinct lines of research involving helper agents in a learning system.
In the first line, we address a collaborator scenario in which the AI agent collaborates with a bounded-rational helper it has never encountered before. The AI agent deploys a Stackelberg game solution without explicitly modeling the follower’s behavior, enabling rapid and efficient teaming.
In the second line, we propose Learning to Help (L2H) for backup helpers, allowing the AI agent to autonomously manage tasks it can solve while delegating unsolvable tasks to backup helpers. Therefore, the AI agent only needs to focus on training for simple, specific tasks. In contrast, backup helpers only need to focus on those tasks the AI agent cannot solve due to legacy devices or limited computational power
The Influence of Family Presence During Resuscitation on Health Care Providers’ Performance
No full textFamily members’ (FM) presence during cardiopulmonary resuscitation (CPR) is recommended, yet it may reduce healthcare providers’ (HCP) performance in patient care (Fernandez et al. 2009). Insufficient personnel resources could further compromise performance. Simulation-based training, followed by structured debriefing, may help HCP develop strategies to minimize any negative impact of FM presence. This study aims to investigate whether simulation-based training can enable resuscitation teams to perform equally well with or without FM present.
Twenty-eight resuscitation teams (56 HCP) from a Swiss University Hospital consented to participate. Each two-person team included one HCP with over 10 years of clinical CPR experience. All teams obtained a pre-course assignment reviewing CPR guidelines and received an orientation to the simulation. After an initial CPR scenario featuring FM presence, HCP participated in a structured debriefing. Teams then performed two additional scenarios - randomized to either FM or no-FM first – without a second debriefing in between. Primary outcome measures (time to first defibrillation and time to onset of chest compressions) were analyzed from video recordings. Secondary outcomes (CPR quality indicators) were calculated by the Laerdal Session Viewer © software.
All 56 HCP (28 teams) completed three scenarios each. A significant learning effect was observed by comparing the same FM scenario before and after debriefing: Time to onset of chest compressions as well as time to defibrillation decreased significantly (p=0.000023/ p=0.000063).
Of the 28 teams, 14 performed the FM scenario first and then the no-FM scenario, while the other 14 did the reverse. Secondary outcome, CPR quality parameters, were analyzed for 26 teams (drop out of 2 teams due to recording problems with the simulator). In the two post-debriefing scenarios, no significant difference emerged between FM presence and no-FM for onset of chest or time to defibrillation.
Our findings demonstrate a significant learning effect regarding both the onset of chest compressions and time to defibrillation, as well as certain CPR quality measures. Crucially, once participants had completed simulation-based training, there was no performance gap between scenarios with or without FM present
Industrial AI-based Workload Performance Analytics: Applications to Mixed Reality Multitasking
No full textImmersive technologies such as augmented and virtual reality are increasingly integrated into our daily lives. As this digital transformation progresses, understanding human reactions to these technologies becomes crucial, particularly in the context of human factors engineering, which prioritizes human safety and well-being. Mixed reality (MR), which blends the physical and virtual worlds, introduces new multitasking possibilities but also presents challenges. One critical aspect is the impact of MR multitasking on human workload, a key performance measure. This research employs an Industrial AI approach, combining traditional machine learning with advanced pre-trained models to develop predictive models for estimating human workload in MR environments. An experiment was conducted in which participants multitasked between a physical and a digital task within a defined timeframe. Workload data, collected via the NASA Task Load Index (NASA-TLX), was used alongside synthetic data generated by a Generative Adversarial Network (GAN) to create an enriched dataset. The combined real and synthetic data were then used to train predictive models, enhancing accuracy. To improve workload prediction, this study integrates pre-trained models such as BERT (Bidirectional Encoder Representations from Transformers) from large language models (LLMs) and CLIP (Contrastive Language-Image Pretraining) from computer vision applications, alongside traditional machine learning techniques like regression and neural networks. Evaluation using the Root Mean Square Error (RMSE) metric demonstrates that the proposed hybrid models incorporating transfer learning and pre-trained models significantly outperformed conventional methods. The deviations between actual and predicted values were minimal, indicating a more reliable workload estimation. This dissertation advances knowledge in human factors engineering by addressing a critical gap in workload prediction within MR multitasking environments. The findings provide insights into human-computer interaction in complex digital settings. Organizations implementing MR technologies can leverage these predictive models to better understand worker workload and optimize conditions for well-being and efficiency
oAβ and oTau-Induced Endothelial Dysfunction in Alzheimer's: Impact on BBB Integrity & Insulin Signaling
No full textAlzheimer’s disease (AD) is a progressive neurodegenerative disorder that has become a serious health and social concern with the increasing aging population. Currently, approximately 47 million people worldwide suffer from AD, and this number is expected to rise to around 131 million by 2050. Growing evidence indicates that vascular dysfunction is a key contributor to the pathogenesis of AD. Cerebral endothelial cells (CECs), one of the major cell types in the brain, play a vital role in angiogenesis, blood vessel formation, and maintaining blood-brain barrier (BBB) integrity. They also mediate insulin signaling within the brain, which is essential for proper brain function. Dysfunction of CECs has been implicated in the pathology of various neurovascular disorders, including AD. Although pathological protein aggregates associated with AD, such as oligomeric amyloid-β (oAβ) and oligomeric tau (oTau), have been shown to impair neuronal and cognitive function, their direct interactions with CECs and the resulting functional effects remain incompletely understood. In this Ph.D. thesis, I demonstrate that oAβ and oTau induce biochemical and biomechanical alterations in CECs, including changes in rolling adhesion mechanics, oxidative stress, inflammatory responses, cytoskeleton reorganization, tight junction protein expression, BBB integrity, and insulin signaling. These alterations are mediated through the RhoA/ROCK and cPLA2 pathways. Collectively, this study advances our understanding of the interplay between neurodegeneration, endothelial dysfunction, and metabolic dysregulation in AD, offering new avenues for therapeutic intervention
Examining Equity in Teachers' Perceptions of Social and Emotional Learning
No full textBoth scientific and nonscientific communities have reported the positive effects of social and emotional learning (SEL) on student development. However, little attention has focused on students’ racial identity and culture or paid attention to adult learning about SEL. Transformative SEL (Jagers et al. 2019) prioritizes practices that promote an explicit focus on culture, identity, agency, belonging, and engagement. This qualitative study examined the alignment amongst transformative SEL and how high school teachers of Black students perceived and implemented SEL. Three research questions are addressed. First, how does transformational SEL align with the ways that teachers of Black students perceive SEL? Second, how does transformational SEL align with the ways that teachers of Black students implement SEL? Third, how does the relationship among transformative SEL, teachers’ perceptions and their SEL implementation practices vary by teacher race? Thirteen teachers completed an online self-report survey, shared SEL curricular artifacts, and participated in individual interviews. Content analysis yielded eight themes about teacher perceptions and implementation of SEL: emotional and behavioral regulation for students, complementary to academic instruction, SEL training and resources needed for teachers, leveraging historical examples, using student-centered approaches, elevating student voice, encouraging student reflection, and supporting student racial/ethnic identity development. The results are analyzed and discussed with regard to implications for the role of race in SEL training and classroom practices, especially in high schools
Wearable Robotic Device Design and Emulation of Control Strategies Using Machine Learning
No full textChapter 1 includes the introduction. Chapter 2 introduces the development of a two-degree-of-freedom robotic ankle exoskeleton designed to assist with both plantarflexion and inversion-eversion. This device established the technical foundation for later studies by demonstrating precise torque control and improving the design of a robotic ankle-foot prosthesis (AFP) emulator for subsequent human-in-the-loop (HIL) experiments.
Chapter 3 presents the first study on prosthetic personalization, applying human-in-the-loop Bayesian optimization (BO) to tune ankle stiffness in individuals with simulated amputation. The findings demonstrate that BO-driven tuning reduces the metabolic cost of walking more effectively than conventional weight-based tuning, motivating the search for alternative, clinically viable cost functions.
Chapter 4 builds on the previous study by introducing the symmetric foot force-time integral (FFTI) as a pressure-based cost function for HIL optimization. The results show that FFTI correlates with metabolic cost and enables rapid, real-time tuning of prosthesis stiffness, improving gait symmetry and reducing walking effort.
Chapter 5 extends this optimization approach to clinical prosthesis fitting by incorporating socket-interface pressure as a reward function for optimization. This study demonstrates that pressure-based tuning significantly improves comfort and gait mechanics, providing an alternative to traditional prosthetic fitting methods.
Chapter 6 shifts focus to gait adaptation, analyzing how transtibial amputees adjust to prosthesis parameters over time. The results show inter-individual differences in adaptation trajectories, suggesting that prosthesis control should be dynamic rather than static, continuously evolving to accommodate long-term motor learning.
Chapter 7 concludes with a discussion on the broader implications of real-time prosthesis personalization, synthesizing the findings from prior chapters and proposing future directions for adaptive prosthetic control. The dissertation culminates in a framework that integrates biomechatronic design with machine learning-based optimization, paving the way for the next generation of intelligent, user-responsive prostheses
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