UMBC Student Collection

Permanent URI for this collectionhttp://hdl.handle.net/11603/33

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Delivery of Tempol from Polyurethane Nanocapsules to Address Oxidative Stress Post-Injury
(ACS, 2025-02-19) Ale, Temitope; Ale, Tolulope; Baker, Kimberly J.; Zuniga, Kameel M.; Hutcheson, Jack; Lavik, Erin
Traumatic brain injuries (TBIs) result in significant morbidity and mortality due to the cascade of secondary injuries involving oxidative stress and neuroinflammation. The development of effective therapeutic strategies to mitigate these effects is critical. This study explores the fabrication and characterization of polyurethane nanocapsules for the sustained delivery of Tempol, a potent antioxidant. The nanocapsules were designed to extend the release of Tempol over a 30-day period, addressing the prolonged oxidative stress observed post-TBI. Tempol-loaded polyurethane nanocapsules were synthesized using interfacial polymerization and nanoemulsion techniques. Two generations of nanocapsules were produced, differing in Tempol loading and PEGylation levels. The first generation, with lower Tempol loading, exhibited an average size of 159.8 ± 12.61 nm and a Z-average diameter of 771.9 ± 87.95 nm. The second generation, with higher Tempol loading, showed an average size of 141.4 ± 6.13 nm and a Z-average diameter of 560.7 ± 171.1 nm. The zeta potentials were -18.9 ± 5.02 mV and -11.9 ± 3.54 mV for the first and second generations, respectively. Both generations demonstrated the presence of urethane linkages, confirmed by Fourier Transform Infrared Spectroscopy (FTIR). Loading studies revealed Tempol concentrations of 61.94 ± 3.04 μg/mg for the first generation and 77.61 ± 3.04 μg/mg for the second generation nanocapsules. Release profiles indicated an initial burst followed by a sustained, nearly linear release over 30 days. The higher PEGylation in the second generation nanocapsules is advantageous for intravenous administration, potentially enhancing their therapeutic efficacy in TBI treatment. This study demonstrates the feasibility of using polyurethane nanocapsules for the prolonged delivery of Tempol, offering a promising approach to manage oxidative stress and improve outcomes in TBI patients. Future work will include testing these nanocapsules in vivo to determine their potential at modulating recovery from TBI.
Supporting Campus Activism through Creating DIY-AT in a Social Justice Aligned Makerspace
(ACM, 2025-01-31) Higgins, Erin; Oliver, Zaria; Hamidi, Foad
Utilizing digital fabrication methods (e.g., 3D printing) has exciting implications for the design and production of customized assistive technology (AT). However, utilizing these tools currently requires a high level of technical expertise as well as time and money investments. Furthermore, facilitating collaboration between end users and makers needs effective and inclusive approaches with shared language and support for asynchronous, dispersed communication of design requirements. While these Do-It-Yourself (DIY) approaches are shown to support end-user agency and furthering technology democratization, research has to yet explore how they can further align with social justice values and practices. We explored these possibilities by facilitating DIY-AT design with students with disabilities, activist staff members, and community members within a university makerspace. By explicitly encouraging participants to consider social justice issues important to them as they engaged in DIY-AT design, we studied the considerations and supports needed for facilitating flexible co-design activities and broader conversations about accessibility barriers at the university. Adopting a transdisciplinary approach, we offer lessons learned about the potential of co-designing DIY-ATs as a way to investigate questions of social justice, inclusion, and access in academic contexts. We show how these created DIY-ATs can be leveraged by students and staff as tangible artifacts to encourage more funding and support from university administration for accessibility initiatives.
Co-designing a 3D-Printed Tactile Campus Map With Blind and Low-Vision University Students
(ACM, 2024-10-27) Crawford, Kirk Andrew; Posada, Jennifer; Okueso, Yetunde Esther; Higgins, Erin; Lachin, Laura; Hamidi, Foad
Blind and low-vision (BLV) university students often encounter campus accessibility challenges that impede their ability to navigate campus environments effectively. The lack of customization offered by some navigational-focused assistive technologies (ATs) often falls short in addressing their diverse and specific navigational needs. 3D printing, a promising tool for creating affordable and personalized aids, has been explored as a method to create customized tactile maps to aid BLV individuals with general navigation. However, the use of 3D-printed tactile maps by BLV university students and the impact of their direct involvement in the design process remain largely unexplored. We employed a participatory design (PD) approach to engage BLV students from a university in the United States (U.S.) through semi-structured interviews and a co-design session to create a prototype 3D-printed tactile map. Additionally, we consulted with a blind rehabilitation and independence expert for insight into their perspective on AT and, more specifically, tactile maps and showed the prototype to a group of visually impaired youth and instructors visiting our university for feedback. We present and discuss our findings, provide an overview of the prototype design process, and outline future work.
An Ecosystem of Support: A U.S. State Government-Supported DIY-AT Program for Residents with Disabilities
(ACM, 2024-10-27) Higgins, Erin; Sakowicz, Marie E.; Hamidi, Foad
While Do-It-Yourself (DIY) approaches to producing customized assistive technology (AT) have been shown to support end-user agency and further technology democratization, research has shown that the utilization of digital fabrication tools requires a high level of technical expertise as well as financial investment. Facilitation of collaborations between end users and makers is a possible solution to these issues, however, previous efforts have uncovered issues around shared language, lack of consistent communication, and liability concerns. A promising direction for addressing these issues is to conceive of new types of multi-organizational collaborations that draw on complementary strengths. We explored these possibilities through an Action Research study in which we collaborated with the Maryland department of disability to launch a state level DIY-AT program. Through developing and supporting this program, we studied motivations for participation, relationships to creating and customizing AT, how individuals participated in and grew the program, and how the program allowed for individuals to reflect on their disabilities and AT use. Our findings generated an ecosystem model describing the interdependent relationships between and roles held by each stakeholder in the state DIY-AT program as well as a description of how this ecosystem encouraged expanding and transcending the understandings and definitions of AT and disability. We offer lessons learned for the design of future government-supported DIY-AT programs and reflections on the role of HCI researchers within these ecosystems.
Decoding the Privacy Policies of Assistive Technologies
(ACM, 2024-10-22) Crawford, Kirk; Khoo, Yi Xuan; Kumar, Asha; Mentis, Helena; Hamidi, Foad
As assistive technologies (ATs) have evolved, they have become increasingly connected. However, these increasing connections pose significant privacy challenges, especially when user privacy is described using complex privacy policies. Our study decodes the privacy policies of 18 ATs to understand how data collection and processing are communicated with users. We find that (1) AT privacy policies are structured to offer legal protections to their companies and not always to protect user privacy, (2) AT privacy policies are absent protections for individuals with disabilities, (3) AT policies are inconsistent when describing data storage, handling, and security methods, (4) AT policies often do not differentiate between essential and non-essential data collection, and (5) there is often a lack of transparency in AT policies around third-party data sharing. These findings reveal that AT privacy policies overlook and underestimate a user?s acceptable privacy risks. We conclude our study by discussing AT design implications.
Advancing climate model interpretability: Feature attribution for Arctic melt anomalies
(2025-02-11) Ale, Tolulope; Schlegel, Nicole-Jeanne; Janeja, Vandana
The focus of our work is improving the interpretability of anomalies in climate models and advancing our understanding of Arctic melt dynamics. The Arctic and Antarctic ice sheets are experiencing rapid surface melting and increased freshwater runoff, contributing significantly to global sea level rise. Understanding the mechanisms driving snowmelt in these regions is crucial. ERA5, a widely used reanalysis dataset in polar climate studies, offers extensive climate variables and global data assimilation. However, its snowmelt model employs an energy imbalance approach that may oversimplify the complexity of surface melt. In contrast, the Glacier Energy and Mass Balance (GEMB) model incorporates additional physical processes, such as snow accumulation, firn densification, and meltwater percolation/refreezing, providing a more detailed representation of surface melt dynamics. In this research, we focus on analyzing surface snowmelt dynamics of the Greenland Ice Sheet using feature attribution for anomalous melt events in ERA5 and GEMB models. We present a novel unsupervised attribution method leveraging counterfactual explanation method to analyze detected anomalies in ERA5 and GEMB. Our anomaly detection results are validated using MEaSUREs ground-truth data, and the attributions are evaluated against established feature ranking methods, including XGBoost, Shapley values, and Random Forest. Our attribution framework identifies the physics behind each model and the climate features driving melt anomalies. These findings demonstrate the utility of our attribution method in enhancing the interpretability of anomalies in climate models and advancing our understanding of Arctic melt dynamics.
Identifying Flaky Tests in Quantum Code: A Machine Learning Approach
(2025-02-06) Kaur, Khushdeep; Kim, Dongchan; Jamshidi, Ainaz; Zhang, Lei
Testing and debugging quantum software pose significant challenges due to the inherent complexities of quantum mechanics, such as superposition and entanglement. One challenge is indeterminacy, a fundamental characteristic of quantum systems, which increases the likelihood of flaky tests in quantum programs. To the best of our knowledge, there is a lack of comprehensive studies on quantum flakiness in the existing literature. In this paper, we present a novel machine learning platform that leverages multiple machine learning models to automatically detect flaky tests in quantum programs. Our evaluation shows that the extreme gradient boosting and decision tree-based models outperform other models (i.e., random forest, k-nearest neighbors, and support vector machine), achieving the highest F1 score and Matthews Correlation Coefficient in a balanced dataset and an imbalanced dataset, respectively. Furthermore, we expand the currently limited dataset for researchers interested in quantum flaky tests. In the future, we plan to explore the development of unsupervised learning techniques to detect and classify quantum flaky tests more effectively. These advancements aim to improve the reliability and robustness of quantum software testing.
Polyurethane Nanocapsules Incorporating Epigallocatechin Gallate, A Green Tea Extract
(Wiley, 2025-02-26) Ale, Temitope; Ghunney, Nhyira; Pandala, Narendra; Tucker, Budd; McFadden, Kassandra; Hutcheson, Jack; Lavik, Erin
Explosions cause 79% of combat-related injuries, often leading to traumatic brain injury (TBI) and hemorrhage. Epigallocatechin gallate (EGCG), a green tea polyphenol, aids neuroprotection and wound healing. In this work, we sought to investigate the fabrication and characterization of polyurethane nanocapsules encapsulating EGCG, demonstrating controlled, on-demand release, and highlighting their potential for targeted therapeutic delivery in trauma care.
Neural network-based surrogate model in postprocessing of topology optimized structures
(Springer Nature, 2025-02-28) Persia, Jude Thaddeus; Kyun Sung, Myung; Lee, Soobum; Burns, Devin E.
This paper proposes a general method of creating an accurate neural network-based surrogate model for postprocessing a topologically optimized structure. When topology optimization results are converted into computer-aided design (CAD) files with smooth boundaries for manufacturability, finite element method (FEM) based stresses often do not agree with the topology optimized results due to changes of surface and mesh density. The conversion between topology optimization derived results and CAD files often requires postprocessing, an additional fine tuning of the geometry parameters to reconcile the change of the stress values. In this work, a feedforward, deep artificial neural network (DANN) is presented with varying architecture parameters that are found for each stress output of interest. This network is trained with the data based on a combination of Design of Experiments (DoE) models that have the geometry dimensions as inputs and stress readings under various loads as the outputs. A DANN-based surrogate model is constructed to enable fine tuning of all relevant stress performance metrics. This method of constructing an artificial network-based surrogate model minimizes the number of FEM computations required to generate an optimized, post-processed design. We present a case study of postprocessing a wind tunnel balance, a measurement device that yields the six force and moment components of a test aircraft. It needs to be designed considering multiple stress measures under combinations of the six loading conditions. Excellent performance of a neural network is presented in this paper in terms of accurate prediction of the highly nonlinear stresses under combinations of the six loads. Von Mises stress predictions are within 10% and axial force sensor stress predictions are within 2% for the final post-processed topology. The results support its usefulness for postprocessing of topology optimized structures.
Individual Versus Group Cognitive-Behavioral Therapy for Partner-Violent Men: A Preliminary Randomized Trial
(Sage, 2020-08-01) Murphy, Christopher; Eckhardt, Christopher I.; Clifford, Judith M.; LaMotte, Adam Douglas; Meis, Laura A.
A randomized clinical trial tested the hypothesis that a flexible, case formulation-based, individual treatment approach integrating motivational interviewing strategies with cognitive-behavioral therapy (ICBT) is more efficacious than a standardized group cognitive-behavioral approach (GCBT) for perpetrators of intimate partner violence (IPV). Forty-two men presenting for services at a community domestic violence agency were randomized to receive 20 sessions of ICBT or a 20-week group cognitive-behavioral therapy (CBT) program. Participants and their relationship partners completed assessments of relationship abuse and relationship functioning at baseline and quarterly follow-ups for 1 year. Treatment uptake and session attendance were significantly higher in ICBT than GCBT. However, contrary to the study hypothesis, GCBT produced consistently equivalent or greater benefits than ICBT. Participant self-reports revealed significant reductions in abusive behavior and injuries across conditions with no differential benefits between conditions. Victim partner reports revealed more favorable outcomes for group treatment, including a statistically significant difference in psychological aggression, and differences exceeding a medium effect size for physical assault, emotional abuse, and partner relationship adjustment. In response to hypothetical relationship scenarios, GCBT was associated with greater reductions than ICBT (exceeding a medium effect) in articulated cognitive distortions and aggressive intentions. Treatment competence ratings suggest that flexible, individualized administration of CBT creates challenges in session agenda setting, homework implementation, and formal aspects of relationship skills training. Although caution is needed in generalizing findings from this small-scale trial, the results suggest that the mutual support and positive social influence available in group intervention may be particularly helpful for IPV perpetrators.
Women's Formal Help-Seeking Before and After Their Abusive Partner Initiates Relationship Violence Treatment
(Sage, 2023-02-01) Murphy, Christopher; Nnawulezi, Nkiru; Ting, Laura
Intimate partner violence survivors (N=122) reported on formal help-seeking before and after their male partners enrolled in a Relationship Violence Intervention Program (RVIP). At baseline, only 20% of survivors had ever received domestic abuse (DA) counseling. DA counseling was more common among survivors with more extensive partner abuse exposures, and for black women residing in suburban versus urban communities. New help-seeking was associated with survivor perceptions of the abusive partner's stage of change. RVIP impact may be enhanced through culturally sensitive survivor outreach that is responsive to a broad range of needs and includes repeated contact over time.
Moderators of Response to Motivational Interviewing for Partner-violent Men
(Springer Nature, 2012-10-01) Murphy, Christopher; Linehan, Erin L.; Reyner, Jacqueline Cooper; Musser, Peter H.; Taft, Casey T.
Careful attention to motivation for change may enhance the effects of interventions for partner-violent men. The present study tested predictions about differential response to a two-session motivational intake (MI) for partner-violent men, which was compared to a structured intake (SI) control. For those who were initially reluctant to change, MI produced greater forward movement in stage of change. For those who claimed to have already solved their problems with partner abuse, MI produced greater backward movement in stage of change and greater homework compliance in subsequent group cognitive-behavioral therapy (CBT). MI led to a stronger collaborative working alliance for those high in contemplation of change, and greater group session attendance for those with high trait anger. Contrary to hypotheses, analyses of post-CBT partner assault revealed a differentially greater benefit of MI for participants with lower levels of pretreatment contemplation and trait anger. The findings provide qualified support for the expectation that MI has the greatest beneficial effects on those who appear initially reluctant to change.
Evaluating Causal AI Techniques for Health Misinformation Detection
(IEEE, 2025-03-17) Clark, Ommo; Joshi, Karuna
The proliferation of health misinformation on social media, particularly regarding chronic conditions such as diabetes, hypertension, and obesity, poses significant public health risks. This study evaluates the feasibility of leveraging Natural Language Processing (NLP) techniques for real-time misinformation detection and classification, focusing on Reddit discussions. Using logistic regression as a baseline model, supplemented by Latent Dirichlet Allocation (LDA) for topic modeling and K-Means clustering, we identify clusters prone to misinformation. While the model achieved a 73% accuracy rate, its recall for misinformation was limited to 12%, reflecting challenges such as class imbalance and linguistic nuances. The findings underscore the importance of advanced NLP models, such as transformer based architectures like BERT, and propose the integration of causal reasoning to enhance the interpretability and robustness of AI systems for public health interventions.
Your smart home exchanged 3M messages: defining and analyzing smart device passive mode
(HAL, 2025-03) Badolato, Christian; Kullman, Kaur; Papadakis, Nikolaos; Bhatt, Manav; Bouloukakis, Georgios; Engel, Don; Yus, Roberto
The constant connectedness of smart home devices and their sensing capabilities pose a unique threat to individuals’ privacy. While users may expect devices to exhibit minimal activity while they are not performing their intended functions, this is not necessarily the case, and traditional idle mode designations are insufficient to address the current landscape of smart home devices. To address this we propose a passive mode designation based on a comprehensive categorization of smart home devices. We then measure the network traffic of thirty-two devices in their respective passive modes. We find that 97% of the devices exhibit near-constant network activity in these modes (exchanging over 3M messages in 24 hours), with many of the devices initiating and responding to LAN communications with other devices, which potentially exposes users to privacy leakages.
Home Literacy and Mathematics in Bulgaria, Israel, Spain, and the U.S.: How Do Preschool Parents Socialize Academic Readiness?
(Springer Nature, 2025-02-08) Stites, Michele; Sonnenschein, Susan; Aram, Dorit; Karabanov, Galia Meoded; López-Escribano, Carmen; Shtereva, Katerina; Krasniqi, Besjanë; Gursoy, Hatice
Previous research shows that preschool parents in the United States (U.S.) prioritize literacy over mathematics, despite the importance of both subjects for their child's future academic success. However, less is known about how parents in other countries socialize the literacy and mathematics skills of young children. This paper examines the beliefs of preschool parents from Bulgaria (N=103), Israel (N=167), Spain (N=138), and the U.S. (N=183). These countries were selected due to differences in location, economics, religions, languages, and alphabet. Specifically, we examine the importance parents place on home literacy and mathematics, the time spent in the home on those activities, and parents' confidence in supporting their child's learning in both domains. We also examined the type of support and resources parents in each country would value receiving from their child's teacher. The results indicated the importance of expanding research from just U.S. participants. Parents from all four countries valued home literacy and mathematics but viewed literacy as significantly more important. While parents from all four countries viewed literacy as more important, differences between countries were noted when it came to the time spent on different subjects, with Spain and the U.S. spending more time on literacy and Bulgaria and Israel spending more time on mathematics. Parents from the U.S. indicated significantly higher levels of confidence in supporting literacy than parents in the other three countries; however, no differences were noted in confidence for supporting mathematics. The types of resources that parents would like to receive also varied by country.
LLM-Supported Safety Annotation in High-Risk Environments
(Open Review, 2025-02-13) Eskandari, Mohammad; Indukuri, Murali; Lukin, Stephanie M.; Matuszek, Cynthia
This paper explores how large language model-based robots assist in detecting anomalies in high-risk environments and how users perceive their usability and reliability in a safe virtual environment. We present a system where a robot using a state-of-the-art vision-language model autonomously annotates potential hazards in a virtual world. The system provides users with contextual safety information via a VR interface. We conducted a user study to evaluate the system's performance across metrics such as trust, user satisfaction, and efficiency. Results demonstrated high user satisfaction and clear hazard communication, while trust remained moderate.
Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization
(2025-02-27) Barron, Ryan; Eren, Maksim; Serafimova, Olga M.; Matuszek, Cynthia; Alexandrov, Boian S.
Agentic Generative AI, powered by Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), Knowledge Graphs (KGs), and Vector Stores (VSs), represents a transformative technology applicable to specialized domains such as legal systems, research, recommender systems, cybersecurity, and global security, including proliferation research. This technology excels at inferring relationships within vast unstructured or semi-structured datasets. The legal domain here comprises complex data characterized by extensive, interrelated, and semi-structured knowledge systems with complex relations. It comprises constitutions, statutes, regulations, and case law. Extracting insights and navigating the intricate networks of legal documents and their relations is crucial for effective legal research. Here, we introduce a generative AI system that integrates RAG, VS, and KG, constructed via Non-Negative Matrix Factorization (NMF), to enhance legal information retrieval and AI reasoning and minimize hallucinations. In the legal system, these technologies empower AI agents to identify and analyze complex connections among cases, statutes, and legal precedents, uncovering hidden relationships and predicting legal trends-challenging tasks that are essential for ensuring justice and improving operational efficiency. Our system employs web scraping techniques to systematically collect legal texts, such as statutes, constitutional provisions, and case law, from publicly accessible platforms like Justia. It bridges the gap between traditional keyword-based searches and contextual understanding by leveraging advanced semantic representations, hierarchical relationships, and latent topic discovery. This framework supports legal document clustering, summarization, and cross-referencing, for scalable, interpretable, and accurate retrieval for semi-structured data while advancing computational law and AI.
An Improved Autoencoder Approach for Nuclei Image Segmentation
(2025-02-26) Ayanzadeh, Aydin
There is a dire need to enable an early diagnosis system to enhance the therapeutic outcome for patients by applying a medical image analysis application. This study proposes an improved auto-encoder model by integrating Squeeze and Excitation (SE) blocks on the different phases of the model for semantic segmentation, which U-Net inspires. We redesigned the model's skip-connection by utilizing Residual Squeez and Excitation (RSE) by employing SE block in a residual way to reduce the semantic gaps and discrepancy between encoder and decoder features. Then, we integrate the Dense Squeeze and Excitation (DSE) block in the model's bottleneck with a densely connected structure. We increase the model's accuracy compared to vanilla U-Net by integrating the discussed module in the model to enhance its capability for feature extraction and obtain more high-level features from the input feature. To evaluate our model's performance, we conducted our experiment on the 2018 Data Science Bowl dataset and compared it with the different approaches that are inspired by U-Net. Our proposed model achieved the Dice and IoU of 92.15% and 85.92% , respectively, surpassing most of the current stateof-the-art models.
Ransomware Evolution: Unveiling Patterns Using HDBSCAN
(CEUR, 45589) Bhandary, Prajna; Joyce, Robert J.; Nicholas, Charles
This research presents an innovative approach to enhancing ransomware detection by leveraging Windows API calls and PE header information to develop precise signatures capable of identifying ransomware families. Our methodology introduces a novel application of hierarchical clustering using the HDBSCAN algorithm, in conjunction with the Jaccard similarity metric, to cluster ransomware into discrete families and generate corresponding signatures. This technique, to our knowledge, marks a pioneering effort in applying hierarchical density-based clustering to over 1.1 million malicious samples, specifically focusing on ransomware and using the clusters to automatically generate signatures. We show that identifying unique Windows API function patterns within these clusters enables the differentiation and characterization of various ransomware families. Furthermore, we conducted a case study focusing on the distinctive function combinations within prominent ransomware families such as GandCrab, WannaCry, Cerber, Gotango, and CryptXXX, unveiling unique behaviors and API function usage patterns. Our scalable implementation demonstrates the ability to efficiently cluster large volumes of malicious files and automatically generate robust, actionable function signatures for each. Validation of these signatures on an independent malware dataset yielded a precision rate of 98.34% and specificity rate of 99.72%, affirming their effectiveness in detecting known ransomware families with minimal error. These findings underscore the potential of our methodology in bolstering cybersecurity defenses against the evolving landscape of ransomware threats
Global Relevance of Online Health Information Sources: A Case Study of Experiences and Perceptions of Nigerians
(2024-11-13) Clark, Ommo; Joshi, Karuna; Reynolds, Tera L.
Online health information sources (OHIS) offer potential for improving access to health information especially in areas with limited healthcare infrastructure. However, OHIS predominantly originates from Western societies potentially ignoring the specific needs and cultural contexts of diverse populations. There is limited research on the global suitability of OHIS content. This study explores the global relevance of OHIS for diverse populations through a case study examining user experiences of Nigerians living in multiple countries. Findings reveal OHIS usage patterns are influenced by the country of residence and local health services availability. The study highlights the need for culturally inclusive OHIS content to ensure equitable health information access globally. Ultimately, for OHIS to serve a global audience effectively, there needs to be reliable information sources that acknowledge and cater to different users' cultural backgrounds, including prevalent health issues, medical practices, beliefs, languages, and healthcare expectations.

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