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Accelerating Innovation: Increasing the Velocity of Ecosystem Development in Higher Education
No full textThe increasing demand for impactful innovation from higher education institutions (HEIs) has elevated their role as engines of economic growth, technological progress, and societal advancement. This dissertation, Accelerating Innovation: Increasing the Velocity of Ecosystem Development in Higher Education, investigates how universities can systematically cultivate high-performance innovation ecosystems capable of producing sustained, market-driven outcomes.
The study is guided by the central question: If an academic institution seeks to build a thriving innovation ecosystem that consistently produces high-value outcomes, what practices, structures, and strategies should be implemented? To address this, a mixed-methods approach integrates nine large-scale data sources, including NSF and NIH funding, SBIR/STTR awards, AUTM technology transfer data, Innovation Development Institute (IDI) company profiles, PrivCo financial outcomes, and institutional metrics. A multivariate modeling framework using SIMCA software was applied to analyze 127 U.S. HEIs from 2013–2023, producing a comprehensive Academic Innovation Performance Index (AIPI).
Key findings reveal that company-focused outcomes, such as the number and performance of academic spinouts, are the strongest indicators of ecosystem health. Multivariate modeling identified a clear set of institutional characteristics positively associated with innovation success: active engagement in SBIR/STTR programs, robust participation in NSF I-Corps, high levels of NSF funding, strong regional partnerships, significant research expenditures, a deep pipeline of PhD-level researchers, a large STEM faculty presence, and substantial on-campus research space. Conversely, heavy reliance on NIH funding, higher transfer-out rates, and weaker local engagement were negatively associated with commercial outcomes.
This work highlights the pivotal role of STTR partnerships, particularly those involving academic spinouts and “super government contractors,” as catalysts for downstream innovation. It also underscores that innovation velocity depends not only on research excellence but also on structured, deliberate ecosystem strategies that bridge on-campus discovery and market translation.
The findings offer a replicable, data-driven framework for assessing and advancing HEI innovation ecosystems. These insights provide actionable guidance for academic leaders, policymakers, and industry partners seeking to strengthen their innovation impact and accelerate the transformation of research into societal value
A Critical Ethnography of Preservice Teachers' Imagined Emotions and Racial Engagement
No full textTeacher education programs in the United States continue to be predominantly white in both faculty and student demographics, with curricular structures that often marginalize or isolate discussions of race and systemic racism (Sleeter, 2017; Milner, 2010). These programs frequently confine issues of diversity to a single multicultural education course. As a result, preservice teachers often enter the profession without having critically engaged with their own racial identities or the broader impacts of systemic racism within educational contexts (King & Butler, 2015; Ladson-Billings, 1999). This critical ethnography, framed by Critical Whiteness Studies (CWS), explores the imagined emotions and anticipated responses of ten preservice teachers as they consider addressing race in their future teaching practice. Findings reveal that while participants commonly experience fear and discomfort when envisioning these engagements. The study underscores the need for teacher education programs to prioritize critical conversations about race and its influence on teacher development
Examination of Fear Extinction in Chronic Cannabis Use, Anxiety Disorders, and their Co-occurrence
Get PDFComorbid cannabis use and anxiety are common, but the relationship is not well understood. The main active ingredient of cannabis is Δ9-tetrahydrocannabinol (Δ9-THC). Acute THC intoxication has been shown to improve between and within-session fear extinction, while chronic THC intoxication impairs fear extinction and discrimination. The current project aimed to determine if chronic cannabis use was associated with worse fear discrimination, extinction (within-session extinction), and extinction retention (between-session extinction) in the anxiety disorder population. 46 participants from three groups (i.e. clinically significant anxiety and cannabis-naïve, chronic cannabis users, or cannabis-naïve healthy controls) completed a 2-day fear differential conditioning paradigm. Results suggest that chronic cannabis-users have an impaired ability to extinguish within-session fear-based responding as compared to the other two groups. There was no evidence of significant group differences in extinction retention or fear discrimination. Findings have important clinical implications as they suggest that chronic cannabis use may impair one’s ability to extinguish fear-based learning, a primary mechanism of exposure-based therapy, which may be a potential barrier to therapy effectiveness
Making the Abstract Physical: Toward Improving Binary Reverse Engineering via Embodied Immersion
No full textPerforming reverse engineering (RE) to determine precisely what a piece of software does is vital to tasks such as securing networks, mitigating malware, and maintaining legacy software. Much software is distributed as binary executable programs, which are particularly difficult for humans to comprehend because the compilation process is a one-way transformation from context-rich source code to a highly-optimized binary program. Our central problem is that binary RE is a highly-specialized skill that requires extensive training and experience. Additionally, the RE process requires a human-in-the-loop because the compound uncertainties introduced in disassembling and decompiling a binary program prevent a fully-automated solution. This work postulates and tests methods to improve the effectiveness of the human-machine joint cognitive system performing sensemaking in the context of binary RE, particularly, leveraging affordances of immersive virtual reality (VR) that exploit facets of cognition typically underutilized in the task of binary RE.
In tackling this problem, we followed a hybrid human-centered interaction design process combining Design Thinking (DT) with Cognitive Systems Engineering (CSE). Within our discovery phase, we performed a thorough interdisciplinary survey providing the theoretical basis for augmented the RE process with immersive VR. In our definition phase, we prioritized the identified affordances in VR into an initial set for the development phase, launching into multiple iterations of build and test of our VR system, Cognitive Binary Reverse Engineering (CogBRE), leveraging feedback from RE practitioners in each iteration.
Based on informal feedback from these iterations indicating that practitioners value the ability to place and organize code fragments and flow graphs in the expanse of VR as they form an understanding of a binary program, we designed and executed a formal user study. Using a between-subjects design, we compared CogBRE (in two VR configurations) to a traditional desktop interface across several metrics, including performance, cognitive load, usability, and user experience. While task accuracy was statistically equivalent across conditions, participants in VR reported significantly lower cognitive load and described meaningful advantages in spatial organization and contextual reasoning.
Recognizing the rise of large language models (LLMs) and spurred by early feedback that users sought novel graph visualizations, we conducted a pilot study using an LLM to generate 3D function call graphs based on self-directed interrogation of disassembled binaries. Evaluators found that the LLM-generated visualizations were often correct, interpretable, and helpful, suggesting future potential for LLMs as collaborative agents in immersive environments.
This work contributes a novel VR system to augment the binary RE process, empirical evidence that embodied spatial interaction can reduce subjective cognitive load, early evidence that LLMs can serve as visualization and reasoning partners, and a generalizable research framework for applying embodied interaction to other cognitively demanding technical domains
Adversarial Attack Detection and Defense in Graph Alignment and Text-to-Image Generation
Get PDFRecent advances in machine learning have highlighted critical vulnerabilities in graph matching models and text-to-image diffusion models (T2I DMs), where adversarial attacks can significantly compromise system performance while remaining imperceptible to users. This dissertation addresses the dual challenges of developing effective adversarial attacks and robust defense mechanisms across two domains: graph matching systems (including network alignment and cross-lingual entity alignment in knowledge graphs) and text-to-image generation models.
Our research tackles fundamental issues in adversarial machine learning: generating effective attacks while ensuring imperceptibility, and developing defenses that maintain system performance. We identify and solve gradient vanishing issues in iterative attack methods and address the challenge of defending against adversarial perturbations without compromising matching or generation quality
Experimental Study of Corrosion in Cables and Development of S-Parameter-Based Non-destructive and In-situ Monitoring Technology
No full textCables are a key part of electrical and electronics systems, responsible for carrying
electricity and signals over long distances. Ensuring their safety and reliability is essential to ensure
electricity and signals are delivered without interruptions. Silver-plated copper cables are widely
utilized in various high-performance applications by NASA, DOE and DOD due to better electrical
and thermal conductivity, higher corrosion resistance, solderability, crimp ability, flexibility and
durability compared to pure copper. However, silver-plated copper cables are highly susceptible
to a specific kind of corrosion called red plague which significantly affects its mechanical and
electrical properties including strength, ductility, fatigue life and electrical conductivity.
Addressing red plague is a significant challenge for many NASA, DOE, and DOD systems.
Thus far, all experimental studies concerning red plague have been conducted exclusively
on cable samples without electrical current applied. However, in real-world applications—ranging
from industrial environments to advanced systems used by NASA and the DOD—these cables are
typically connected via solder joints, routinely carry electrical current, and operate for extended
periods under varying atmospheric conditions. As regards the current direction in-plane to the
copper-silver interface, it is well understood that the current itself will not affect the corrosion.
Therefore, by best knowledge, there is no scientific finding/report on the corrosion in cables
carrying DC current and the impact of current on corrosion. This approach aligns with current
scientific knowledge, which suggests that the rate of corrosion growth should not be affected by
the electrical current itself in the cables. As a result, investigating the specific impact of direct
current (DC) on corrosion in silver-copper cables under real-world conditions is both urgent and
essential.
Currently, corrosion detection and monitoring techniques for corrosion under coating and
insulation such as red plague are mainly destructive which involves peeling of the insulation layer
for visual inspection of cables resulting in material damage, cable wastage, and significant
corrosion-related costs. This is because detecting red plague, which occurs specifically in silver
plated copper cables, presents unique challenges as the corrosion develops internally at the
interface between the silver plating and the copper core, making it difficult to identify using
conventional non-destructive testing methods. Therefore, it is important to develop a non
destructive corrosion detection and monitoring technology to minimize catastrophic system
failures and reduce the high costs associated with corrosion.
In this work, red plague in silver-plated copper cables with and without DC current was
experimentally studied and characterized under 90oF and 90% relative humidity atmospheric
condition using optical microscopy (OM), scanning electron microscopy (SEM), energy dispersive
x-ray spectroscopy (EDS) and nano x-ray CT techniques.
Firstly, the corrosion rates/depth in cables with and without DC current including
longitudinal and transversal was experimentally determined. The atmospheric spread/depth of
corrosion for long term periods was predicted using corrosion models. The influence of DC current
on the corrosion growth in cables was also experimentally exploited. The study revealed that DC
current significantly accelerates corrosion, causing red plague to occur earlier, grow faster, and
cover a larger area. Corrosion was also observed to spread along the cables in the direction of DC
current. In addition, corrosion at the current input end (positive) was also found to be more severe
than output end (negative) of the cables. Corrosion also initiates earlier and progresses faster in
cables carrying higher current values or subjected to higher current densities.
Furthermore, this research determined that the corrosion acceleration observed in cables
with DC can be attributed to self-hall effect phenomenon. In silver-plated copper cables with DC
current, the self-hall effect is believed to cause the electrons to drift away from the cable surface
under the influence of Lorentz force, creating localized regions of electron depletion promoting
anodic reactions and making the regions more susceptible to corrosion. Moreover, the Hall effect
phenomenon in copper conductors was experimentally verified in this study, further supporting
these findings.
Finally, a novel time-dependent s-parameter-based non-destructive and in-situ technology
was developed to evaluate the corrosion status in the silver-plated copper cables. Two techniques
were developed to effectively represent the corrosion status in the cables, namely the loss function
and peak analysis method. The loss function approach involves utilizing loss function formulas,
specifically mean squared value, to compute the numerical difference differences of the s
parameter readings at various time points. The peak analysis method involves using a Fast Fourier
Transform smoothing and counting zero crossing using softwares for counting the number of peaks
within specific frequency ranges.
Ultimately, this dual approach – integrating experimental study and non-destructive
technology – will not only provide a way to assess the operational readiness of the system in which
silver-plated copper cables are used but would facilitate a substantial reduction in system failures
and associated costs due to corrosion
Balancing the Benefits and Challenges of AI: Understanding its Impact on Employee Work Engagement and Burnout
No full textThis thesis explored how artificial intelligence (AI) impacts work engagement and
burnout, using the Job Demands-Resources (JD-R) model to examine AI as both a job resource
and demand. Key AI factors included Perceived Adaptability, Quality, Personal Utility, AI use
anxiety, and AI-induced job insecurity. The study investigated how personal resources moderate
the AI–well-being relationship. Employees with higher personal resources may better leverage
AI for performance, while those with lower resources may experience AI as a greater demand.
Multi-wave cross-sectional data were collected from employees across industries utilizing AI.
Measures included the Utrecht Work Engagement Scale, Shirom-Melamed Burnout Measure,
and scales for personal resources. Data analysis involved multiple regression and moderation
tests, with post hoc probing for significant effects. Results revealed that perceived quality and
utility of AI positively predicted work engagement, while AI use anxiety significantly predicted
burnout. Findings offered insights into AI's dual role in shaping employee health
Clomiphene Citrate Supplementation During In Vitro Maturation of Porcine Oocytes Reduces ROS Accumulation and Promotes Oxidative Stress Resilience Pathways
No full textThe research in this thesis addresses key challenges in reproductive biology particularly
concerning oocyte maturation and early embryonic development. The efficiency of in vitro
reproductive technologies such as oocyte maturation and development, in porcine lags behind
other livestock species such as cattle. We aim to explore the potential effect of the selective
estrogen receptor modulator (SERM) clomiphene citrate (CC) on porcine oocyte maturation and
subsequent in vitro embryo development. Through exploring the effects of CC on porcine oocyte
maturation and development we seek to provide new insights to advance the use of in vitro
reproductive technologies in pigs.
The first study in this thesis investigated the effects of (CC) on porcine oocyte maturation.
We investigated nuclear stage of maturation and ROS at 22 h and 44 h in CC treated and control
oocytes, neither parameter was affected by CC at 22 h. At 44 h, there was also no significant
different in nuclear maturation, but treated oocytes demonstrated significantly reduced levels of
reactive oxygen species (ROS) at 44h, suggesting improved cellular conditions during in vitro
maturation (IVM) at this time point. To further investigate the biological mechanisms behind this
effect, the mRNA expression of genes related to oxidative stress (SOD1, SOD2, GPx) and
apoptosis (BAX, CASP3) was analyzed using RT-PCR, with GAPDH and RNA18S as reference
genes. Despite the observed reduction in ROS, gene expression analysis did not show any
statistically significant differences between treated and control groups.
To further investigate the molecular mechanisms underlying improved oocyte quality
following clomiphene citrate (CC) treatment, a second study was conducted to achieve deeper
molecular characterization through transcriptomic analysis. We sequenced RNA from 44 h in vitro
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matured porcine oocytes treated with CC for the first 22 h and their untreated counterparts. After
filtering out low-expression transcripts, 15,921 genes were retained from a total of 20,391, and
510 differentially expressed genes (DEGs) were identified using a p-value threshold of ≤ 0.05 and
|log2 fold change| > 0.5. Among these, 391 genes were upregulated and 119 were downregulated
in CC-treated oocytes. ClueGo was used to run a pathway enrichment analysis and among those
significantly enriched, two were most notable: the phosphatidylinositol signaling system, and
inositol phosphate metabolism—each essential to oxidative damage mitigation, mitochondrial
regulation, cell cycle progression, and intracellular signaling fidelity. The activation of key
signaling mediators such as AKT1, MAPK3, and INPPL1 reinforces the notion that CC modulates
critical pathways associated with oocyte survival and maturation. These findings demonstrate the
potential of CC as a functional additive in IVM systems aimed at improving reproductive outcomes
in livestock.
In our third study, we investigated the effects of Clomiphene citrate (CC) on early
embryonic development. Consistent with the previous studies, CC was applied during IVM of
oocytes, and we subsequently assessed whether this exposure influenced the ability to undergo
successful activation and early embryonic development using parthenogenic activation. Nuclear
assessment revealed that oocytes that had been treated during maturation had significantly higher
cleavage rates at 30 hours post-activation (38.43%±9.28) compared to controls (26.86%±8.08%,
p = 0.022), suggesting enhanced developmental readiness and possibly healthier embryos. There
were trending significant differences observed between groups in embryo classification at 60
hours, with more treated oocytes cleaving, and reaching past the 8-cell stage (p<0.1) indicating
that CC treatment has promise to benefit cleavage timing and increase the number of transferable
stage embryos in vitro. To further explore underlying mechanisms, we assessed the expression of
genes related to oxidative stress (SOD1, SOD2), apoptosis (BAX, CASP3), and pluripotency
(POU5F1) using RT-PCR, with GAPDH and RNA18S as reference genes. No significant
differences were detected in the tested genetic markers in embryos formed from treated and control
oocytes, suggesting that CC’s effects are not mediated by changes in expression of these target
genes. These results support CC’s potential to enhance early embryo development without
inducing cellular stress or compromising embryo development, in vitro
Shaping the Ecological Thought: Ambient Poetics in the Long Nineteenth Century
No full textThis dissertation considers the link between long nineteenth-century literature and ecological thinking, offering ways to study our interrelation and influence with land, water, biotic life, and each other. The main authors in my study are William Wordsworth, Ebenezer Elliott, George Eliot, Gilbert White, Charles Darwin, George Perkins Marsh, John Ruskin, Richard Jefferies, William Morris, and Edward Carpenter. These mostly nineteenth-century writers developed and deployed their work in steadily ecological ways, often as tools to advance ways to reposition an ecological framework that reestablishes the self in place-based rhetoric, addressing contemporary environmental problems and new ways of living in, thinking about, and representing the natural world. My argument is that ambient poetics are a tool to navigate the ways in which literary worlds are shaped and reframed from our own worlds, providing us both with new ways to perceive our interrelatedness and offering a methodology for how we may consider our role in shaping ecological thinking for others and ourselves today. Thus, this dissertation is divided into ambient poetic types: ambient flora, land, water, fauna, and ecotopes. Ecotopes are a framework upon which biotic communities spatially exist, and ambient types comprise the novel’s flora/fauna, landform, rocks/soil. These chapters explore the connections between writers, their ambient depictions, and the movements which they represented or shaped such as railroad expansion protests, Anti-Corn Law leagues, afforestation, Millthorpe, and the Coal Smoke Abatement Society. By reading both the formation and legacy of ambient poetics in the shaping of ecological thought even today, this dissertation then seeks to trace a green language of resistance to ecological harm. More specifically, the dissertation considers the how ambient poetics demarcate ecological urgencies of the long nineteenth century, how writers complicated traditional ways of seeing the world and shaped or inspired interventional movements, and finally how these writers mobilized greater ecological thinking then and now
Machine Learning for Community-Based Intervention Studies: Predicting Post-Program Levels of and Changes in Relationship Quality Following Couple Relationship Education Participation
No full textWith the understanding that applying machine learning methods to study predictors of Couple Relationship Education (CRE) program outcomes could be particularly helpful for identifying critical factors for CRE program designs, the current study used a racially and economically diverse sample of CRE participants to investigate the most important predictors of post-program levels of and changes in relationship quality over the time of one year following CRE program start. Using 79 possible predictors that included participant characteristics, relationship history, relationship skills and practices, individual functioning, and class contextual factors, random forest models identified that up to 33% of the variance in CRE program participants’ post-program levels of relationship quality at one year following program-start, and using 158 possible predictors, 17% of the variance in their changes in relationship quality one year after program-start could be predicted by several, key relational processes variables, individual functioning variables, and family environment variables, regardless of individual characteristics and other contextual variables. Models provided information on the most salient 20-26 factors and align well with a recent framework used for guiding key topics for Couple Relationship Education (CRE). The top five predictors for post-program levels of relationship quality were self-reported satisfaction with dyadic coping in the relationship, partner’s dyadic coping, family harmony, partner’s conflict management, and knowledge of partner. For changes in relationship quality following CRE participation, the top five predictors were self-reported satisfaction with dyadic coping in the relationship, partner’s dyadic coping, acceptance of partner, partner’s negative health control messages, and family environment chaos. Self-reported individual mental health was another important predictor, uniquely accounting for additional variance in changes in relationship quality but not post-program levels of relationship quality. This study offers critical information for skill-based CRE program designs. The current study demonstrated the feasibility of applying machine learning methods to community-based CRE studies and further validated the content emphasis on a broad spectrum of relational processes in skill-based CRE programs. Research and practical implications were discussed