UTSA Runner Research Press (Univ. of Texas at San Antonio)
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Printable Zinc Oxide UV / X-Ray Sensor for Radiation Monitoring
No full textIonizing radiation, such as X-Rays and short wavelength UV light (<280nm e.g.), presents significant risks to many human activities, such as carcinogenesis in living organisms, degradation of mechanical properties of materials, and single-event upsets in electronics. While significant efforts have been made to develop effective solutions for radiation monitoring, many solutions lack the scalability needed to detect radiation events in real-time across distances greater than a few centimeters. In this work, a prototypic radiation-sensitive printed sensor based on the photoexcitation mechanism of semiconducting zinc oxide (ZnO) is characterized under UV and X-Ray conditions and found to be repeatable and dose-responsive. Underlying this work is the progress of a Reactive Inkjet-Printed Electronics (RIPE) approach, which involves the development and evaluation of a reactive zinc oxide precursor using Hansen Solubility Parameters, X-Ray diffraction, photonic processing, and electrical characterization. This work paves the way toward low-cost, large-area, surface-integrated radiation detection, which could contribute to improved radiation monitoring in various environment.Electrical and Computer Engineerin
Fischer–Tropsch Synthesis: Effect of CO Conversion over Ru/NaY Catalyst
No full textUnlike on Fe and Co catalysts, the CO conversion effect on Ru catalyst performance is little reported. This study is undertaken to explore the issue using a series of Ru/NaY catalysts under 200–230 °C, 2.0 MPa, H<sub>2</sub>/CO = 2, and 10–60% CO conversion in a 1 L continuous stirred tank reactor (CSTR). The results are comparatively studied with those of Fe and Co catalysts reported previously. The NaY support and four 1.0%, 2.5%, 5.0%, and 7.5% Ru/NaY catalysts were characterized by BET, H<sub>2</sub> chemisorption, H<sub>2</sub>O-TPD, XRD, HRTEM, and XANES/EXAFS techniques. The BET and XRD results suggest a high surface area (730 m<sup>2</sup>/g), high degree of crystallinity of the NaY support, and high dispersion of Ru, while an hcp Ru structure and well-reduced Ru were reflected in the HR-TEM FFT and XANES/EXAFS results. The reaction results indicate that the CO conversion effect on CH<sub>4</sub> and C<sub>5+</sub> selectivities on the Ru is the same as that on the Fe and Co catalysts, with CH<sub>4</sub> selectivity decreasing and C<sub>5+</sub> selectivity increasing with increasing CO conversion. However, the CO conversion effect on olefin formation for the Ru catalyst was found to be opposite to that of the Fe and Co; increasing CO conversion enhanced olefin formation but suppressed secondary reactions of 1-olefins. The H<sub>2</sub>O cofeeding experiments showed that H<sub>2</sub>O impacted olefin formation by suppressing hydrogen adsorption and hydrogenation. The H<sub>2</sub>O-TPD experiment evidenced a much stronger H<sub>2</sub>O adsorption capacity (6.8 mmol/g-cat) on Ru followed by Co (1 mmol/g-cat), and then Fe (0.2 mmol/g-cat)., which showed only a very low H<sub>2</sub>O adsorption capacity.This finding may explain the opposite CO conversion effect on olefin formation observed on the Ru catalyst, and may also explain why low CH<sub>4</sub> selectivity (i.e., 3%) occurred on the Ru catalyst and high CH<sub>4</sub> selectivity (i.e., 6–8%) occurred on the Co catalyst, both of which possess low water gas shift (WGS) activity.Mechanical EngineeringBiomedical Engineering and Chemical Engineerin
Mid-wave infrared laser absorption tomography of nitrogen oxides for radially-resolved thermochemistry
No full textA tomographic laser absorption spectroscopy technique, using mid-wave infrared light sources, is presented as a quantitative method to spatially resolve the mole fraction and temperature in small-diameter reacting flows relevant to combustion of nitrogen-based fuels and propellants, with particular applicability to the study of green propulsion concepts. Tunable quantum and interband cascade lasers are used to spectrally resolve multiple rovibrational transitions near 4420 nm and 5180 nm to measure nitrous oxide, nitric oxide, and water mole fractions, as well as gas temperature in an axially-symmetric hydrogen-nitrous oxide premixed jet flame. Signal processing methods for direct nitrous oxide thermometry utilizing a Boltzmann regression are detailed for the experiment, including novel considerations for the tomographic reconstruction of axial and radial profiles of thermochemical structure for the flame. The tomographic absorption spectroscopy technique is demonstrated to recover radially-resolved nitrous oxide, nitric oxide, and water mole fractions for multiple planes at different heights above the jet exit, revealing distinct reaction zones in the jet flame associated with the production of each water and nitric oxide surrounding the relatively cool reactant core containing nitrous oxide.Mechanical Engineerin
Non-Epistemic Reasons as Insufficient for Belief
No full textThis thesis argues that non-epistemic reasons are insufficient for epistemically justifying belief. Its purpose is to demonstrate that because belief inherently aims at accurately representing truth, only reasons reliably connected to truth can provide genuine epistemic justification. The method involved several steps. It established belief's truth-oriented nature and defined epistemic justification based on this aim. The study then argued that non-epistemic reasons, including comfort, utility, faith, and hope, lack the necessary connection to truth. It also critiqued prominent philosophical defenses of non-epistemic justification from thinkers like James, Bishop, and Talbot. The thesis concludes that non-epistemic reasons are insufficient for epistemic justification. Their use leads to epistemically unjustified beliefs. These beliefs undermine the integrity of belief and knowledge. They do this by detaching belief from truth-tracking and confusing practical justification with epistemic warrant.Philosoph
Enhanced resource allocation in elastic optical network using deep learning and optimization process
No full textThe elastic optical network offers several advantages in bandwidth allocation compared to traditional fixed-grid optical networks. These advantages stem from their ability to flexibly and efficiently allocate resources, meeting modern communication networks’ dynamic and diverse demands. It is crucial to handle dynamic traffic loads and proactively manage the resources in an elastic optical network with a productive technique. Deep learning is an effective tool for complex data analysis and real-time decision-making. We address a model that integrates two deep neural networks: generative adversarial network (GAN) for data augmentation; and echo state network (ESN) for network’s requirement prediction. Furthermore, an optimization process is carried out for efficient spectrum allocation. The GAN provides a considerable and reliable quantity of data necessary to train the ESN model that could provide the desired output. The ESN model is further enhanced by optimizing the essential parameters, enabling it to learn diverse traffic patterns and anticipate unusual situations. By using a GAN-ESN approach, there is a substantial benefit in reducing latency, saving energy, and optimizing bandwidth allocation. The simulation results confirm that the proposed scheme can significantly improve the performance of resource management and achieve a high degree of fairness(95%accuracy) in the evaluation metrics.Electrical and Computer Engineerin
Reviewing ceramic material distribution in Terminal Classic, Chichen Itza
No full textDuring the Promeza Chichen Itza Archaeological Project (2022-2023) field season, a total of 87,312 sherds from three excavation fronts have been analyzed and respectively classified with the Type-Variety system (Smith and Gifford 1966, Smith 1971, Robles 1990). With the assistance of ArcGIS pro as a georeferencing we will be able to spatially display the ceramic findings in order to detect material concentrations in the excavated structures. The ceramic clusters as well as their components might prove useful to recognize not only high concentration dumpsters but, possible zones of activity such as the storage, processing and serving of food.Anthropolog
Chingadera
No full textThe full text of this item is not available at this time because the author has placed this item under an embargo until November 15, 2025.Chingadera is a speculative fiction narrative that follows a Choctaw woman exiled from Earth and imprisoned on a lunar labor colony after an apocalyptic collapse. Suppressing her heritage to survive, she forms an unlikely bond with Mineral, a rogue android. A tragic event transforms Chingadera into a cyborg, awakening her rage and ancestral power. Growing antlers and embracing her identity as Deer Woman, she destroys the predatory overseer and escapes the colony. Blending Indigenous myth with futurist rebellion, Chingadera explores survival as resistance, reimagining the cyborg not as progress but as a spiritual rupture that defies colonial and technological control.
This thesis engages with key works of fiction and research to reimagine Indigenous survivance through the lens of science fiction, Indigenous futurism, and feminist resistance. Set against a backdrop of collapsed timelines and weaponized memory, Chingadera integrates Gloria Anzaldúa’s borderlands theory, Gerald Vizenor’s survivance, and Tobias C. van Veen’s “Armageddon Effect,” among others, to interrogate colonial temporarily and techno-utopian narratives. The lunar colony, suspended near a black hole, becomes a metaphor for erasure and stagnation, while Chingadera’s body becomes a site of refusal. Drawing from speculative traditions, including Isaac Asimov, Ted Chiang, Philip K. Dick, and Cherie Dimaline, the thesis resists redemptive arcs in favor of insurgent endurance.Englis
Converting Autoencoder Based Energy-Efficient and Secure DNN Inference on Edge Devices
No full textDeep neural networks (DNNs) have become essential for computer vision tasks like image classification, object detection, and depth estimation. With the rise of embedded devices, there is a growing demand for lightweight and energy-efficient models. While DNNs outperform traditional machine learning methods, their deep architectures pose challenges regarding energy consumption, latency, and computational efficiency. Despite various optimization techniques, there is still room for improvement in making these networks more efficient. We introduce a novel approach to DNN compression using autoencoders, leveraging the idea that not all images require the same level of complexity for classification. Our method trains an autoencoder to transform complex images into simpler representations, enabling a more efficient DNN. To optimize this process, we incorporate intraclass clustering on complex datasets, minimizing reconstruction loss and improving performance. This allows for the selective elimination of higher DNN layers, ensuring that the model meets its Service Level Objective (SLO) targets for various edge devices like Raspberry Pi and Jetson Nano. We also repurpose feature extraction layers from a baseline DNN as encoder layers while designing a decoder that reconstructs features in a way that simplifies classification. Instead of replicating input features, the autoencoder generates a more easily classifiable representation, enhancing accuracy while reducing computational overhead. By balancing efficiency and performance, our approach outperforms existing techniques, achieving the desired inference
latency without compromising accuracy. As AI adoption continues to expand, more DNNs are being trained and deployed on third-party platforms, increasing security risks. Models operating in untrusted environments are particularly vulnerable to adversarial attacks, where small input v perturbations can lead to incorrect predictions. We have thoroughly examined both white-box attacks, which have full access to model parameters, and black-box attacks, which operate without knowledge of the model’s internal structure. To address these threats, we developed a detection framework that identifies compromised or Trojan inputs by analyzing activation patterns, entropy values, and reconstruction loss. By leveraging autoencoders to examine intermediate feature representations, our system effectively differentiates between benign and adversarial inputs, enhancing model security. Our goal is to build DNNs that are not only efficient but also robust against adversarial threats, ensuring both performance and security for real-world AI applications.Computer Scienc
Io’s Neutral Cloud Observations with HST’s Cosmic Origins Spectrograph
No full textIo’s neutral cloud, composed primarily of oxygen and sulfur species, plays a crucial role in shaping the plasma environment around Jupiter. This neutral material escapes Io’s diffuse SO2 atmosphere and interacts with the plasma torus, supplying mass and energy to Jupiter’s magnetosphere. Using the Hubble Space Telescope’s Cosmic Origins Spectrograph, we detected statistically significant neutral oxygen (OI) emissions in the far-ultraviolet extending up to 36 Io radii from the volcanic satellite. These observations allow us to constrain the spatial distribution of the neutral oxygen cloud, improving our understanding of its composition and evolution. Our findings show that Io’s extended neutral cloud is composed of atomic oxygen and is excited by electron impacts. The interaction between torus ions and neutral species from Io alters the surfaces and atmospheres of the other Galilean satellites. Our results contribute to a more complete picture of neutral-plasma interactions in the Jupiter system, serving as a foundation for future studies of magnetospheric processes and planetary atmospheres. Further investigations using the OI] 135.6 nm/OI 130.4 nm line ratio will refine our understanding of dominant excitation mechanisms and constrain the column density of neutral oxygen around Io. These insights have broad implications for planetary plasma interactions beyond the Jovian system.Physics and Astronom
Media Exposure, Risk Perception, & Efficacy: Intentions & Behaviors Regarding Climate Change
No full textClimate change is an issue at the forefront of the world and many individuals’ minds. In a college setting, in which students are actively working on degrees that help secure their future, climate change inherently calls into question the sanctity of this same future. Based on the theory of planned behavior, specifically regarding the influence of norms (media exposure as norms), attitudes (risk perception as attitudes), and perceived control (climate change efficacy as perceived control) on intentions and behavior, this study investigated the effect of media exposure on risk perception regarding climate change, how media exposure affects feelings of climate change efficacy, how both risk perception and climate change efficacy then influence intentions for pro-environmental behavior, and finally, whether pro-environmental intentions influence actual behaviors. What was found was that norms—media exposure—does not appear to have as much of an effect on attitudes—risk perception—and perceived control—climate change efficacy—as previously thought, whereas risk perception and efficacy do in fact have strong relationships with intentions, and intentions have a strong relationship with behaviors. Further studies should be conducted to more closely look at the relationship media has with these variables, as well as what consuming media means in the modern day.Psycholog