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Aerodynamic Performance Enhancement of Small Fixed-Wing Unmanned Aerial Vehicles through Bio-Inspired Microfiber Surfaces
Get PDFThis work focuses on the application of an innovative passive flow control technique, utilizing microfiber coatings with diverging pillar cross-sections, inspired by gecko feet, on the wing of a small unmanned aerial vehicle (UAV). The starting point in the study was the development of a small-scale modular UAV to explore the concept of reconfigurability. In this process, a multidisciplinary approach was taken, integrating computational fluid dynamics (CFD), structural analysis of 3-D printed structures, and wind tunnel testing for validation. This UAV demonstrated versatility across different mission profiles, with low-speed and high-speed variants optimized for endurance and time-critical missions. Later, the same UAV model was used as a test apparatus for the bio-inspired microfiber research. Wind tunnel experiments in static and dynamic conditions were conducted to evaluate the coatings' effects on aerodynamic performance and control surface authority. In static tests, the microfiber pillars significantly reduced drag, with up to a 24.7 reduction at the cruise Reynolds number. Control surface effectiveness, particularly in pitching moments, saw marked improvement, especially near stall conditions, with an increase in control authority of up to 22.4. Dynamic tests revealed enhanced damping characteristics at low angles of attack, which translates into higher maneuverability in terms of normal acceleration production per control surface input. By combining the novel microfiber flow control approach with reconfigurable UAV design, this work highlights potential advancements in UAV performance, advocating for further research in both areas to optimize aerodynamics and structural integrity for diverse operational requirements
The Effects of Reference Frames on Help-Seeking Intentions of College Students Using Learning Analytics Dashboards
No full textThis study investigates the impact of reference frames on reflective and adaptive help-seeking intentions among college students using Learning Analytics Dashboards within the context of self-regulated learning. The study employs an experimental design to explore how different reference frames—self-referenced, and peer-referenced—affect students' help-seeking attitudes. The participants were 121 undergraduate students in the US. Findings indicate no significant differences in help-seeking intentions across the different reference frames. There were moderate positive correlations between the help-seeking intentions, resources, and mediums
SWCPC 403 E2 #10 Brunk's Comedians Show Band - A. C. Hefner, Charles Myers, Harry Goldie, Harley Sadler, Roy Shelley, and others, undated.
Get PDFQuantification, fate, and transport routes of per- and polyfluoroalkyl substances (PFAS) in diverse environmental compartments
No full textPer- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants (POPs) and are ubiquitous in the environment worldwide. These contaminants, used widely across many industries due to their stability and unique physiochemical properties, are now known to cause negative effects on human and environmental health. There is a need to understand how this expansive class of chemicals is impacting the ecosystems they are found in and how they are behaving in these environments in order to inform remediation efforts and protect exposed organisms.
In this dissertation, the transportation, fate, and uptake of PFAS were studied at two historically contaminated locations via sampling of the biota, soils and sediments, and groundwater. One set of studies focused on the uptake, transport, and bioaccumulation of PFAS in biota over several years, while the other focused on quantification of PFAS in different environmental samples and groundwater by developing soil extraction and high resolution passive sampling methods.
While we cannot yet model exactly how PFAS will interact with their surroundings due to the large number of factors influencing behavior that are site-specific, including the distribution and use of different AFFF formulations, co-contaminants, geophysical properties of local soils and sediments, groundwater influence, and habits of organisms living in contaminated locations, these studies provide essential information on the behavior and distribution of PFAS at environmentally relevant concentrations and in directly affected ecosystems. This work contributes to the growing body of knowledge about PFAS in our environment and will aid in developing remediation efforts in the coming decades
A Phenomenological Study of Lubbock Independent School District’s Teachers’ Perceptions of Teaching with the Agricultural Education Sciences Approach
No full textThis phenomenological study explores the experiences of agricultural science teachers within Lubbock Independent School District’s Agri-STEM program. Through semi-structured interviews with seven teachers, key themes emerged, highlighting the collaborative nature of the program and its integration of academic and practical skills across disciplines. Teachers viewed the Agri-STEM approach as a valuable tool for enhancing students' academic performance and personal growth, with a particular focus on critical thinking, leadership development, and career readiness. However, the study also identified challenges, including limited administrative support and resource constraints, which impact program delivery. These findings accentuate the importance of robust partnerships, resource allocation, and ongoing professional development to improve the efficacy of agricultural science education within the STEM framework
Quantum Enhancements for Direct Search Methods
No full textThis dissertation explores the use of quantum search algorithms to im-
prove direct search methods for single and multiobjective optimization. Quantum search algorithms are known to reduce the expected number of oracle calls needed to find the solution of an unstructured search problem from O(N ) to O(√N). However, this reduction relies on the existence of a solution and these algorithms are not well equipped to determine when no solution exists. Due to their probabilistic nature, an exhaustive search is not guaranteed to occur using any finite number of oracle calls. This poses a significant hurdle when using them for direct search methods, where convergence requires accurate searches to be performed
during each iteration. For those unfamiliar, we provide a brief review of quantum computing and quantum search algorithms in chapter 1.
In chapter 2, we develop a classical-quantum hybrid algorithm for use
within a type of direct search method called Generalized Pattern Search (GPS). These methods generate a sequence of iterates by systematically searching for a point that provides a reduced objective function value. The points chosen for consideration are determined by the outcome of the most recent search, with convergence of the algorithm requiring the search results to be accurate. We prove that our algorithm accurately identifies a point with reduced objective function value using O(√N) zeroth-order oracle calls, when one exists, otherwise correctly determining that no such point exists using O(N) oracle calls.
In chapter 3, we extend the approach used in chapter 2 to the multi-
objective variant of GPS called Direct MultiSearch (DMS). In this setting, the
iterates of GPS are replaced with sets of non-dominated points. Iterate updates are performed by adding a candidate point to the incumbent set and removing all dominated points from the result. We prove that our algorithm accurately performs this update, identifying an element of the incumbent set that dominates the candidate point using O(√N) calls to oracles for the objective functions or identifying all elements of the incumbent set dominated by the candidate point using O(N) oracle calls
From Symbolic Reasoning to Object Embeddings: Advanced Approaches of Knowledge Distillation in Compacted Neural Networks
No full textThe rapid advancement of neural networks has profoundly impacted industries worldwide. However, challenges arise when these networks are deployed on devices with limited resources. The two major impediments are the computational power required and the storage demands. To address these challenges, domain knowledge has emerged as a gateway to reducing the gap between architecturally distinctive networks. Among the various domain knowledge integration mechanisms, this dissertation explores Knowledge Distillation (KD), a process where knowledge extracted from a high-performance network is incorporated into a compact neural network. This approach enables the compact neural network to enhance its generalization capabilities while performing tasks that would otherwise be unattainable due to its limited architecture. The dissertation also examines the integration of logical reasoning which addresses the shortcomings of current data-driven machine learning approaches. Such data-driven learning approaches are regarded as ``black-box" which often lack adequate reasoning behind their predictions. By combining logical reasoning with knowledge distillation, this dissertation demonstrates significant performance enhancements for resource-constrained neural networks.
This dissertation begins by providing a foundational approach to incorporating domain knowledge into machine learning, offering a roadmap that enhances traditional learning mechanisms. The core of this dissertation is the development and refinement of knowledge generation strategies. It starts with the creation of knowledge through weighted ensembles of logits derived from multiple high-performing models leading to accuracy improvements in predicting images in a multi-class image classification task. Additionally, the use of weighted ensemble of intermediate models further boosts the capabilities of compact models. This dissertation also introduces a novel neurosymbolic approach to knowledge distillation, which combines symbolic reasoning with sub-symbolic neural processing to establish a robust learning system.
Diverging from earlier approaches, the latter part of this research explores the use of Concept Activation Vectors (CAVs) to enhance both interpretability and performance. These CAVs, derived from a high-performing model, are integrated into compact networks using a neurosymbolic approach. This integration creates a sophisticated framework that not only improves the interpretability of the distilled knowledge but also extends its applicability to image classification tasks. The class-specific features, converted into concept activation vectors improve performance in compact models, serving as an effective form of knowledge transfer.
The final part of the research expands on the idea of knowledge distillation with object embeddings within a multi-modal architecture. Differing from the previous approaches, this work applies knowledge distillation for visual question-answering (VQA) tasks. Instead of relying on learning from raw pixels, this research focuses on generating rich knowledge of the images through object embeddings. These embeddings are composed of specialized features derived from regions of interest, structural attributes, and spatial relationships. The incorporation of such rich knowledge ensures enhanced performance on small networks.
This dissertation offers contributions by introducing advanced techniques in knowledge distillation that optimize the performance and interpretability of compact neural networks. By focusing on integrating domain knowledge and incorporating logical reasoning, the research demonstrates how compact models can achieve enhanced functionality and greater generalization capabilities. The use of Concept Activation Vectors (CAVs) and object embeddings highlights the potential for these models to perform effectively in different applications, such as image classification and Visual Question Answering (VQA). The implication of this provides a pathway toward more efficient and interpretable AI systems that can be successfully deployed in resource-constrained environments
More Than Horror, Other Than Fantasy: Stephen Graham Jones, Tanya Tagaq, and Decolonial Genre Fiction
No full textIn this project, I turn to Chadwick Allen’s (Chickasaw ancestry) trans-Indigenous methodology to consider how two Indigenous North American authors move within Euro-Western genre, or move beyond it altogether, in making fictive texts that advance decoloniality and Indigenous artistic sovereignty. First, I read Stephen Graham Jones’s The Only Good Indians as a novel that adopts the exterior form of the mainstream slasher genre only to destabilize and indigenize the genre’s interior logic—its standard characters, narrative arcs, and tropes. By indigenizing this literary form, Jones not only undermines its conventions but also shows how it is founded on and perpetuates settler violence, how it serves as an optics for reading settler expansionism in the U.S. as slasher horror, and how it resonates with narratives that authorized the killing of Indigenous peoples as part of the settler project. Next, I turn to Tanya Tagaq’s written text Split Tooth and the music video for her track “Colonizer.” In the ways these texts portray kinship between Indigenous peoples and the other-than-human world, they function as what Daniel Heath Justice (Cherokee) calls Indigenous wonderworks. Read together, as what I call a trans-media Indigenous wonderwork, they support one another in bolstering Tagaq’s message of decoloniality and Indigenous artistic sovereignty. Allen’s trans-Indigenous methodology shows that, when juxtaposed—or read close together—Jones’s and Tagaq’s texts ultimately act as lenses or tools for interpreting Indigenous cultural production that works within and beyond mainstream Euro-Western genres
High variability in flood discharge and stage accelerates river mobility
Get PDFLateral channel migration is a fundamental process in natural alluvial rivers; however, the factors that control the rate of migration remain unclear. Despite its importance in shaping river morphology, the impact of water discharge on river mobility is still largely unexplored. Here, we leverage a dataset of 64 rivers across the globe to show that higher variability in river discharge and stage promotes higher rates of river migration. To reveal the physical processes behind this relationship, we focused analyses on the lowermost 500 kilometers of the Mississippi River, where a pronounced gradient in water stage variability and migration rate exists. We demonstrate that stage variability affects channel mobility by influencing the sediment size of riverbanks and thereby controlling riverbank erodibility. These results can be used to predict river responses to climate change and decipher past hydroclimates using stratigraphy from Earth and Mars.This work was supported by the Yonsei University Research Fund, Post-Doctoral Researcher Supporting Program, #202112-0018 (C.W.); National Science Foundation of Korea, NRF- 2023R1A2C100763111 (W.K.); US Geological Survey under grant/cooperative agreement no. G21AC10765 (F.T.-C.T.); US National Science Foundation award no. 2019561 (F.T.-C.T.); and US National Science Foundation award no. 1952814 (T.Y.D.)
Exploring the Effectiveness of the PLC at Work® Process in Texas Schools
Get PDFThis report summarizes findings from a three-part evaluation of Model PLC at Work® schools in Texas. Using linked statewide administration data from the University of Houston Education Research Center (UH-ERC), the evaluation examined the characteristics of schools achieving Model PLC at Work® designation, the impact of sustained implementation on student academic outcomes, and the effects on teacher turnover and retention. Results indicate that Model PLC at Work® schools are located across diverse geographic regions and employ more traditionally certified teachers, demonstrate lower turnover rates, and maintain a more veteran teaching workforce compared to statewide averages. Student achievement analysis of elementary and middle schools reveal consistent gains in both math and reading, equivalent to two to three months of additional learning, with especially strong effects for economically disadvantaged students in math and English learners in reading. Teacher workforce analysis further highlights stronger retention of highly effective teachers in Model PLC at Work® elementary, middle, and high schools, suggesting that the PLC at Work® process supports both student learning and the stability of instructional quality. Together, these findings demonstrate that the PLC at Work® process, when implemented with fidelity as found in Model PLC at Work® schools, is associated with measurable improvements in student outcomes and retention of highly effective educators.
This research was supported by funding from Solution Tree. The findings and conclusions presented
are those of the author(s) and do not necessarily reflect the views of the funding
organization