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Shock Tube Investigation And Chemical Kinetic Modeling Of Green Propellants For Space Missions
No full textHydrazine thrusters have been used for decades to land spacecraft on other planets. While its use is common, handling hydrazine is difficult due to its toxicity; this poses a risk to humans during testing and loading the propellant onto a spacecraft. Occupational Safety and Health Administration has limited human exposure to 1 part per million (ppm) of hydrazine per eight-hour workday. Green propellants are promising alternatives because they have lower toxicity but do not sacrifice performance. Green propellant technology has been demonstrated in space and future missions are planned, such as NASA’s Green-Propulson Dual-Mode technology demonstration. At the University of Central Florida, laser absorption spectroscopy measurements were conducted during shock tube ignition delay time measurements. Ethylene and nitrous oxide were diluted in argon gas and shock heated to 1400 to 1600 K and 12 bar. A 4.9 μm laser measured carbon monoxide, nitrous oxide was measured by a 4.6 μm laser, and nitric oxide was measured by a 5.2 μm laser. The species\u27 time histories were compared to the predictions of the NUIG 1.2 chemical kinetic mechanism. Predictions of the model can be improved by comparing it to the experimental data. The work will lead to increased confidence in the operation of space thrusters using green propellants
Contagion Chronicles: A Spatiotemporal Analysis of Armed Conflict and Infectious Disease
No full textThis dissertation examines the relationship between armed conflict and infectious disease transmission through a novel theoretical framework that integrates ecological disturbance and health systems resilience theories into epidemiological contexts. The framework conceptualizes diseases along a continuum from pulse to press. Pulse diseases, like Ebola, are characterized by sudden onset, high mortality, and short-term outbreaks. Press diseases, like HIV, progress gradually with lower mortality and long-term persistence. Grounded in this pulse–press disease typology and ecological and health systems resilience, the theory suggests that the absorptive capacity, adaptive capacity, and resilience of community health workers in conflict areas can influence disease transmission, depending on whether a disease follows a pulse or press pattern.
Several advanced spatiotemporal statistical techniques are used to investigate the impact of armed conflict on disease transmission across African countries between 2000 and 2018. To address gaps in Ebola reporting, a modified inverse distance weighting interpolation method was developed, incorporating variables such as fruit bat habitat distribution, outbreak timing, geographic proximity, regional context, and precipitation patterns. A spatially autocorrelated zero-inflated negative binomial model accommodates overdispersed and spatially dependent Ebola data. To evaluate the impact of conflict on HIV spread, a generalized linear model incorporating spatial basis functions was used to correct for spatial structure and autocorrelation in the data.
The empirical analysis suggests the relationship between conflict and disease operates through more complex mechanisms than the pulse and press theoretical framework predicts at the country-year level. Although the hypothesized direct pathways linking conflict events to pulse and press disease transmission patterns do not reach statistical significance, the methods provide a foundation for future research in several directions: examining dynamics at sub-national scales; applying spatial basis functions to better capture geographic dependencies; and developing modified interpolation techniques to estimate missing disease data across time and space
The Effects of Resistance Training Volume on Muscle Quality Among Older Adults
No full textBACKGROUND: Age-related declines in muscle strength are more closely linked to deteriorations in muscle quality than muscle mass. Echo intensity (EI) measured by B-mode ultrasonography is a common non-invasive marker of intramuscular adiposity and fibrous tissue. Although high-intensity resistance training is effective, the added value of higher training volumes on muscle quality and strength in older adults remains unclear. PURPOSE: This study aimed to compare the effects of different resistance training volumes on various measures of muscle quality, size, and strength among older adults. METHODS: Twenty-five older adults were randomized to either moderate volume (2 sets per exercise) or high volume (6 sets per exercise) resistance training groups. Participants trained twice weekly, performing leg extension, trap-bar deadlift, and leg press at 85% of one-repetition maximum (1RM). Pre- and post-intervention assessments included ultrasonographic measures of the vastus lateralis (VL) and rectus femoris (RF), leg lean mass, leg extension 1RM, isometric and isokinetic quadriceps strength, and functional tests. RESULTS: Group × time interactions were small, suggesting no advantage of high volume training. Regardless of volume, participants improved in VL EI (p = 0.025, ηp 2 = 0.200), VL (p = 0.015, ηp 2 = 0.232) and RF cross-sectional area (p \u3c 0.001, ηp 2 = 0.440), leg extension 1RM (p \u3c 0.001, ηp 2 = 0.794), isometric torque (p \u3c 0.001, ηp 2 = 0.630), and concentric torque at 180°/s (p \u3c 0.001, ηp 2 = 0.423) and 300°/s (p = 0.009, ηp 2 = 0.263). No changes were observed in RF EI, leg lean mass, or functional performance. CONCLUSION: Higher training volumes did not confer additional benefits over moderate volumes for improving muscle quality. EI changes were muscle-specific, suggesting heterogeneous adaptations among older adult
Transformation to Re-Engagement: A Temporal Analysis of TEFL Tourism Experiences
No full textThis study investigates the psychological and behavioral transformation resulting from long-term cultural immersion, using TEFL (Teaching English as a Foreign Language) tourism as a lens to examine how deep, lasting change unfolds. While prior literature has primarily classified TEFL as a niche form of tourism, its potential to facilitate enduring personal transformation has not been empirically assessed. This research addresses that gap by applying Transformative Learning Theory and the Model of Transformation within the Stimulus-Organism-Response (S-O-R) framework to explore how TEFL experiences lead to transformation, satisfaction, and long-term re-engagement. Uniquely, the study also examines two underexplored temporal moderators – duration of stay and elapsed time since experience completion – to assess how time influences the sustainability and nature of transformation.
Methodologically, the study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) on survey data collected from 203 former and current TEFL participants. The model integrated experiential triggers and cognitive/affective mechanisms to predict transformational outcomes such as self-change, behavioral (conative) change, and re-engagement intentions. Moderation analyses using interaction terms in SmartPLS tested whether the strength or direction of key structural relationships varied across temporal conditions.
Findings confirmed that transformation occurs across multiple dimensions, with time serving as a significant contextual boundary condition. Duration of stay strengthened the effects of challenging experiences and internal reflection processes, while elapsed time since completion revealed patterns where effects either weakened or reversed. Four significant interaction effects were identified for duration of stay, and seven for elapsed time, reflecting both partial and full moderation patterns. Direct effects further indicated that both recency and immersion length were positively associated with transformational outcomes. Collectively, these results underscore the dynamic, time-sensitive nature of transformation and establish TEFL tourism as a valuable context for understanding sustained psychological change over time
Carleman Linearization of Dynamical Systems, and Graph Inverse and Wiener Filtering
No full textCarleman linearization is a mainstream approach for transforming finite-dimensional nonlinear dynamical systems into infinite-dimensional linear systems. It provides accurate approximations of the original nonlinear system over larger regions around the equilibrium for longer time horizons, surpassing the capabilities of conventional first-order linearization. In Chapter 1, we derive explicit error bounds for the finite-section approximation, demonstrating its exponential convergence concerning the finite-section order. We illustrate that for a specific class of nonlinear systems, exponential convergence can be achieved across the entire time horizon, extending to infinity. These results hold significant practical utility, as the proposed error bounds can be used to determine appropriate truncation lengths for applications like model predictive control and reachability analysis for safety verification.
As graph signals have gained significant attention and applications in recent years, investigating inverse filtering procedures for graph signal recovery becomes crucial. A key challenge lies in the potentially exponential computational cost associated with direct matrix inversion. Chapter 2 proposes iterative polynomial approximation algorithms that facilitate the distributed implementation of an inverse filter. These algorithms enable the distributed implementation of an inverse filter using a polynomial graph filter with commutative graph shifts. The proposed algorithms exhibit exponential convergence properties, and they can be implemented on distributed networks in which agents are equipped with a data processing subsystem for limited data storage and computation power, and with a one-hop communication subsystem for direct data exchange only with their adjacent agents. Chapter 3 presents Wiener filters for the recovery of deterministic and wide-band stationary graph signals from noisy observations. We then propose distributed algorithms to implement these Wiener filters. Furthermore, the proposed algorithms can be implemented on distributed networks in which agents are equipped with a data processing subsystem for limited data storage and computation power, and with a one-hop communication subsystem for direct data exchange only with their adjacent agents
Extravehicular Activity Propulsion Control Using Body Movement-Based Control Interface
No full textThe objective of the thesis is to address the shortcomings of the current control modality of the Simplified Aid For EVA Rescue (SAFER) system by designing, implementing, and testing a body movement-based control interface, thereby allowing both hands to be free during Extravehicular Activity (EVA). Specifically, head rotations in different directions were captured using wearable inertial measurement units mounted on the head and torso for orientation control of SAFER, and the rotation of ankle joint angles from both legs was captured similarly for translation control of SAFER
The HAI-IO Model: A Framework for Understanding the Human-AI Communication Process
No full textThe increasing integration of artificial intelligence (AI) into daily life calls for new theoretical frameworks that capture human-AI interaction’s dynamic, feedback-driven nature. Traditional models treat AI as a passive medium, overlooking its adaptive capabilities. This paper proposes the Human-AI Interaction Outcomes (HAI-IO) model, an interdisciplinary framework synthesizing human-machine communication, social exchange theory, dialogue systems, and computational feedback models like cybernetics and reinforcement learning. The HAI-IO model frames interaction as iterative and bidirectional—AI adapts through predictive processing while users adjust based on AI feedback. This mutual adaptation shapes trust, engagement, and system optimization. The model informs AI system design, user education, and policy, advocating for adaptive interfaces and ethical oversight. It advances theory and practice in building responsible, responsive AI communication systems
Interactions Of Anti-Viral Ceria Nanoparticles With An Enveloped RNA Virus
No full textThere is growing interest in developing durable and broad-spectrum antiviral nanomaterials for surface disinfection. Silver-modified ceria nanoparticles (AgCNPs) are regenerative materials capable of inactivating a wide range of viruses through generation of reactive oxygen species (ROS). We hypothesized that for enveloped RNA viruses, the lipid bilayer and viral spike glycoproteins are determinants of sensitivity to inactivation of infectivity by AgCNPs. This hypothesis was tested using Vesicular Stomatitis Virus (VSV), a model enveloped negative-sense single strand RNA virus containing a single spike glycoprotein G. Five rounds of serial passage of VSV in the presence of suboptimal AgCNP concentrations led to the emergence of a virus population which had gained resistance to inactivation by AgCNP. Growth time courses and realtime assays for cell killing showed that the resistant VSV population had gained the properties of accelerated growth kinetics and enhanced cytopathic effects compared to the parental AgCNPsensitive virus. VSV derived from a range of cell lines was generated to test the role of varying viral lipid envelope in AgCNP sensitivity –the various cell line-derived VSV did not substantially differ in sensitivity to AgCNPs. However, the presence of biological interfering media, such as fetal bovine serum (FBS) and red blood cells (RBCs), transiently reduced nanoparticle efficacy depending on reaction time and concentration of soil load. Taken together, our findings demonstrate that an enveloped RNA virus can evolve resistance to AgCNPs under sustained selective pressure and that this acquired AgCNP resistance is associated with other accelerated properties of the virus. These results provide important insights into nanoparticle–virus interactions in real world scenarios and the potential for resistance emergence, informing the future development of nanomaterial-based antiviral strategies
Fit Index Criteria For Multisample Analysis in SEM with Ordinal Data: A Monte Carlo Simulation Study
No full textMeasurement invariance is crucial for valid group comparisons in structural equation modeling (SEM), yet testing invariance becomes challenging when using ordinal data. This dissertation evaluates the adequacy of widely used fit index difference criteria (ΔCFI, ΔRMSEA, ΔSRMR) for multisample invariance testing with ordinal indicators. Traditional Δ fit cutoff thresholds were established based on continuous, normally distributed data, but ordinal measures can distort invariance conclusions. Monte Carlo simulations address this issue by manipulating a comprehensive set of conditions: sample sizes (200, 400, 1000), group size ratios (equal vs. unequal), model complexity (low vs. high), underlying distributions (normal vs. nonnormal), Likert scale formats (3-, 5-, 7-point), and varying measurement noninvariance patterns (metric vs. scalar level, with different proportions and magnitudes of item bias). The simulation employed robust estimation methods (ML with Satorra-Bentler correction and WLSMV), which are appropriate for ordinal data. The findings demonstrated that ΔCFI and ΔRMSEA were more reliable indicators of invariance, particularly for scalar noninvariance. Using empirically derived cutoffs from ROC analysis, both indices showed improved sensitivity and specificity. The optimal thresholds were more stringent than the conventional 0.01 and 0.015 values, indicating that standard criteria may underestimate group differences with ordinal data. In contrast, ΔSRMR proved less reliable: it exhibited inflated false-positive rates across most conditions. It often lacked the power to detect true invariance violations. Fit index performance also depended on study conditions. Smaller samples and mild measurement differences often produced negligible changes in fit indices, whereas large samples made the indices overly sensitive to trivial misfits. Additionally, ΔCFI and ΔRMSEA were more effective in detecting scalar noninvariance, whereas ΔSRMR showed some utility in identifying metric-level differences in simpler models. These results support the use of refined cutoffs and robust estimation methods that account for sample size, distribution, and model complexity to improve measurement invariance testing in SEM
Scalable Embedded Parallel System With Implementation of Multi-node Encryption
No full textThe advancements in artificial intelligence (AI) and other innovative technologies promote a demand for high-performance processing systems, underscoring the importance of parallel computing. As a result, the application of parallel systems has spread across various domains. Most of the existing parallel systems are implemented on Field-Programmable Gate Arrays (FPGAs), which require complex designs and adjustments from the user and are often costly despite some performance benefits. This work explores an alternative approach by implementing a parallel system with a distributed memory protocol on affordable and commercially available microcontroller platforms to provide a scalable and cost-effective system. The proposed parallel system architecture contains a memory unit and a set of two or three processing nodes to increase hardware scalability. The design establishes an interconnected array of these modules to facilitate parallel computation. The implementation uses semaphores to enable process synchronization, identification, memory access control through a locking mechanism, and ID assignment. Encryption is used to demonstrate software scalability and the communication protocol of SPI connectivity. Each processing node executes computational tasks using encryption algorithms to convert input messages into encrypted text using the node’s localization and identification as keys. This setup showed increased efficiency compared to conventional single-core processors. However, the current software scalability architecture remains limited due to project time constraints, suggesting potential expansion in future research. Additionally, the proposed architecture can be expanded into three-dimensional arrangements, offering a promising approach to minimizing spatial requirements, substantially increasing scalability, and improving throughput by reducing data transmission distance