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Alteration of Kerogen Wettability Due to Compositional Change in Shale by Interaction with Fluid
No full textRelative permeability modifiers (RPMs) can selectively reduce the permeability of water while maintaining or improving the permeability of hydrocarbon. Understanding the impact of fluid interactions on kerogen wettability alterations near a created hydraulic fracture is particularly important in gas reservoirs. This study investigated the impact of fluid interactions on kerogen wettability alterations in fracturing fluid invasion zones and their consequent effects on gas-liquid relative permeability. To address this, the study conducts experiments and develops models to understand and quantify the changes in kerogen properties and flow dynamics due to these interactions. The research involves three main tasks. First, experiments are performed to quantify the impact of fluids on kerogen wettability alterations. To achieve this objective, kerogen isolates from various organic-rich shales with different types and maturities are mixed with various fluids, including hydraulic fracturing fluid, brine, and deionized water, at a temperature of 80 °C for 14 days. Approaches such as sessile drop method for contact angle measurement, Ion Chromatography (IC), Attenuated Total Reflectance Fourier-Transform Infrared spectroscopy (ATR-FTIR), and Rock-Eval pyrolysis are conducted to determine the alteration of kerogen wettability and geochemistry. This phase determines the mechanisms of kerogen wettability change. Second, an empirical model is developed to represent the contact angle as a function of time. Furthermore, an analytical model is developed to describe gas-water flow behavior as injected fluid invading the gas shale formation, by employing fractal theory and quadratic Hagen-Poiseuille equation and considering time-varying contact angle incorporated into the boundary conditions. The impact of variables such as porosity, wetting phase thickness, wettability, and gas rarefaction on the flow behavior is discussed. This part provides a comprehensive understanding of gas and water flow behavior, highlighting the importance of contact angle and wettability as controlling factors. Third, two numerical models at different scales are developed to explore the effects of wettability changes on the gas-water displacement flow. The centimeter-scale model incorporates time-varying contact angles obtained from experimental data as boundary conditions. Meanwhile, the micrometer-scale models, derived from SEM images of shale samples, set initial gas-water distributions according to the analytical model, i.e., gas occupying the center and water as a thin layer along the wall for hydrophilic conditions, and vice versa for hydrophobic conditions. Slip effects were included due to the small scale. In addition, wettability alterations were also taken into account. The simulations produce relative permeability curves, which are compared with the predictions from analytical models. Additionally, the effect of liquid-gas viscosity ratio and rate of contact angle change on the relative permeability was investigated. As such, this dissertation encompasses a workflow that identifies the kerogen wettability alteration due to fluid interactions and their influence on gas-water flow in fracturing fluid invasion zones, utilizing experimental analysis, analytical modeling, and numerical modeling approaches. The study determines the mechanisms of kerogen wettability alteration, quantifies changes in wettability over time, proposes an analytical model for gas-water flow with time-varying boundary conditions, and simulates gas-water displacement in shale pores from SEM images. By incorporating these findings, this research provides crucial insights into the kerogen wettability alteration due to interaction with injected fluids near hydraulic or secondary fractures
Perinatal Nutrition Knowledge and Sources of Information Among Recently Postpartum Women Experiencing Homelessness: A Convergent Parallel Mixed-Methods Study
No full textWomen experiencing homelessness are particularly at risk for suboptimal nutrition intake during the perinatal period (before, during, and after pregnancy), with challenges that reduce access to affordable, healthy food. To improve nutritional intake, it is critical to establish how and to what extent this population knows about perinatal nutrition. This study examines what recently pregnant women experiencing homelessness know about perinatal nutrition and how they obtain their nutritional information. Women experiencing homelessness within 2 years postpartum in the greater Houston, Texas area were surveyed and interviewed in 2024 for this convergent parallel study. In this ongoing pilot study, preliminary qualitative and quantitative data were collected simultaneously with 8 women recently pregnant and experiencing homelessness. Descriptive statistics and exploratory thematic analysis were integrated to examine perinatal nutrition knowledge in this population more holistically. Participants' mean age was 24.4 years (SD 5.7, range 18-35), with 75% having 2 or more children. Themes included knowledge about what to eat (e.g., avoiding alcohol, eating fruits and vegetables), information sources (e.g., family members, friends, and prenatal health care providers), strategies (e.g., word of mouth, internet, pamphlets, demonstrations), and current preferences related to perinatal nutrition (e.g., preferring more detailed information sooner and other resources on where to get healthier foods). However, only 38% received prenatal care in the first trimester; 25% did not receive prenatal care with their recent child. As data collection continues, findings will identify knowledge gaps and optimal sources and strategies to deliver preferred nutritional education content in the context of unique housing-related structural factors.Health and Human Performance, Department ofHonors Colleg
Ocular and Systemic Effects of Oral Caffeine in Young Adults
No full textPurpose: Oral administration of 7-methylxanthine (7-MX), an adenosine receptor blocker and metabolite of caffeine, slows axial elongation and myopia progression in children. In infant rhesus monkeys, oral 7-MX and 1.4% caffeine eye drops slow experimental myopia. This study investigated ocular and systemic effects of oral caffeine in young adults to understand potential mechanisms of action of adenosine receptor blockers in the eye. Methods: Healthy adults were recruited to participate in two experimental sessions each, in which either a 200 mg compounded caffeine or placebo pill was administered. Baseline measurements included right eye axial length and pupil size (LenStar), autorefraction and accommodation (Grand Seiko), OCT imaging (Spectralis), intraocular pressure (iCare), blood pressure, heart rate, and the Stanford Sleepiness Scale. Measurements were repeated at 1, 3, and 6 hours after ingesting the pill. After 1-2 weeks, the alternate treatment was given. For each time point, mean arterial pressure, mean ocular perfusion pressure, and choroidal thickness were calculated. Repeated measures ANOVAs were used to determine if significant differences existed between placebo and caffeine. Results: Participants (N = 18) were 25.3 ± 5.4 years (8 males, 10 females). Spherical equivalent refraction of right eyes was –3.46 ± 2.25 D and axial length was 25.32 ± 1.23 mm. Caffeine caused a significant increase in mean arterial pressure (P = 0.04) and intraocular pressure (P = 0.009) over 6 hours compared to a placebo pill. There were no significant effects of caffeine on subjective sleepiness, heart rate, accommodative amplitude, mean ocular perfusion pressure, pupil diameter, axial length, or choroidal thickness (P > 0.05 for all). However, regardless of the type of pill ingested, there was a significant change in mean ocular perfusion pressure, pupil diameter, and axial length over 6 hours (P < 0.05 for all). Conclusion: Oral caffeine caused an increase in intraocular pressure and mean arterial pressure compared to placebo pills in young adults. In contrast to previous reports, there were no effects of caffeine on axial length or choroidal thickness. Further research is needed to explore the long-term effects of caffeine and other adenosine receptor blockers on ocular physiology and myopia progression
Evaluating the Effect of Coffee Powder During Curing Process of Cement Paste
No full textGlobally, more than 20 million tons of coffee powder go into landfills annually. Without being treated coffee powder releases greenhouse gas to the environment. Conversely, concrete is the second most-used material in the world, only after water. Concrete is made from cement and aggregate. Cement is one of the main producers of carbon dioxide emissions because of the kiln process. There will be benefits if coffee powder can replace a partial amount of cement when making concrete. This research is to monitor the effect of coffee powder on the cement curing process to determine if coffee powder can be used as an admixture to cement paste.Civil and Environmental Engineering, Department ofHonors Colleg
Make Hay Weyl The Sun Shines: Inquiries in Surface Electromagnetic Transport of Weyl Semimetals
No full textWeyl semimetals are gapless materials whose valence and conduction bands touch at discrete points that are topologically protected. These points where the bands touch are called Weyl points and require either the time-reversal symmetry or inversion symmetry to be broken. The presence of Weyl points in the bulk of the semimetal creates unique features on the surface, such as zero energy states called Fermi arc states. These states connect the projections of Weyl points on the surface, and at the projection of the Weyl points, they bleed into the bulk, making it difficult to write an independent surface Hamiltonian. This leads to great difficulty in exploring the surface transport physics of a Weyl semimetal. The core of this dissertation is two works which bypass the surface inseparability problem by using a Green’s function-based approach: • In the first work, we studied the surface superconductivity of a bi-layered model Hamiltonian of a T-Weyl semimetal with adjustable Fermi arc shapes and found that for a parametrically large finite regime, it is possible to have a case where the surface of the Weyl semimetal is superconducting, but the bulk is in a normal state. This result was used to answer questions raised by superconductivity experiments performed on NbP and PtBi2. • In the second work, we numerically studied the photogalvanic effect in magnetic Weyl semimetal Co3Sn2S2, using a model Hamiltonian. We find that the presence of magnetization gives tunability to the symmetries of the system, which in turn can be used to manipulate the photogalvanic current. Due to the symmetries, the photogalvanic current in the x-y plane is zero, and outside the plane, the current can be flipped by flipping the internal magnetization
Synthesis of Lunar Horizon Imagery Using Generative Models
No full textImages of landscapes and horizons can be a valuable way to estimate or visualize unknown terrain, such as the lunar surface. Since there are limited global datasets of every single possible horizon from the lunar surface, we need synthetic imagery to fill the gap. However, current generative models struggles to produce horizons that accurately represent real-world features. This report addresses the initial steps in generating realistic horizon imagery, which can be vital for advancing planetary imaging techniques and furthering our understanding of unknown landscapes. The work involved fine-tuning and training a Generative Adversarial Network (GAN) model, supported by a heavily preprocessed dataset of Apollo lunar images segmented with a DINOv2 model. A novel graphical user interface was developed to enable real-time interaction with the image generation process. Preliminary findings indicate that the Pix2PixHD model can produce visually and scientifically accurate lunar horizon images. These early contributions lay the groundwork for further development, with future work focusing on integrating georeferenced Lunar Reconnaissance Orbiter (LRO) data to automate the generation of accurate, diverse lunar landscape images.Computer Science, Department ofHonors Colleg
Executives’ Attacks on Courts in Latin America: Definition, Reasons and Impact on Public Confidence in the Judiciary
No full textWhat is an executive attack on courts? Why do executives engage in such attacks, and what are their consequences for public confidence in the judiciary? This dissertation examines these three questions by analyzing executive-judiciary relations in Latin America from 2009 to 2018. While the region has remained predominantly democratic since the third wave of democratization, it continues to struggle with low institutional quality and frequent interbranch crises, which undermine the overall quality of democracy. To address these issues, this study first theoretically defines executive attacks on the judiciary, offering a broad framework that encompasses prior conceptualizations while systematically categorizing different types of attacks. Using this framework, this dissertation introduces a new dataset on executive attacks across Latin America (2009-2018). It then investigates the determinants of these attacks, finding evidence that presidents’ prior political experience in office and a lack of connections with traditional parties are positively associated with such behavior. Finally, the study assesses the public opinion consequences of executives’ attacks on courts, showing correlational evidence that attacks polarize perceptions of the judiciary among government opponents and supporters. This dissertation makes several contributions. First, it provides a novel and comprehensive definition of executive attacks and introduces a unique dataset that systematically records these events, offering a more detailed and empirical account than existing studies. Second, it highlights the role of presidential political experience and party ties, often overlooked systematically in executive-judiciary conflicts. Finally, by studying executive attacks on courts and public opinion, this research contributes to a growing literature on judicial legitimacy, democracy backsliding, and partisan attitudes toward democratic institutions
Spatial Computing Frameworks for Adaptive Disaster Response and Visual Place Recognition
No full textIn the immediate aftermath of a disaster, acquiring information regarding the status of surviving infrastructure is imperative. However, data collected during this period often exhibits inconsistencies, omissions, and errors. We have designed multiple systems to proficiently validate and amalgamate crowd-sourced paths during disasters. One is DeimosBC, a novel post-disaster crowd-sourcing system which relies on a blockchain to provide robustness and decentralization, and the ability for multiple disparate users to contribute their effort to a collaborative task. The other is Proteus, which generates a traversable map from volunteer collected data in a post-disaster scenario. Given a set of collected GPS trajectories per volunteer Proteus can combine them to obtain a single set of connected edges that represent the ground truth quickly and accurately, independent of how the data is collected. Precise geolocation on the Lunar surface is crucial for future exploration, scientific research, and potential human settlement on the Moon. Unlike Earth, the Moon lacks a global positioning system (GPS) infrastructure, presenting significant challenges for accurate positioning and navigation. One potential solution to provide geolocation on the Moon is Visual Place Recognition (VPR), which involves matching one or multiple image sets in order to determine which images show the same places in the world. As there is a severe lack of lunar imagery to train our models on, we have created a model, Despina, for synthesis of high-fidelity location-specific and elevation-realistic Lunar horizon imagery using generative models and digital elevation models. We adapted existing traditional VPR algorithms to account for the unique visual characteristics of the lunar surface, to create a model called Galatea. Galatea uses modified Sequential Delta Descriptors to ignore irrelevant parts of the image (i.e. the common background), while effectively capturing and describing important parts and their relationship within the scene (e.g. how many rocks or craters and where they are in the scene). For feature extraction, we fine-tune the state-of-the-art DINOv2 model with Apollo imagery, which leads to fast convergence and gives us stronger predictive power for our descriptors. Finally, we use the Optimal Transport approach for image retrieval/matching as well as feature matching
Teaching AIs to Reason and Code, Confidentially
No full textLarge Language Models (LLMs) have advanced rapidly, creating new opportunities for automating complex software-engineering tasks, yet today’s models still produce semantically flawed code and raise safety, privacy, and lock-in concerns on centralized clouds. I present an end-to-end framework that teaches AI to reason about code and executes it on a decentralized, privacy-preserving infrastructure. At the modeling layer, I orchestrate a quorum of specialized LLM agents. A Director LLM coordinates a concept agent rooted in programming-language theory, language-specific experts, and a compiler-driven feedback loop. Implementations such as UniTranslator and Smartify deliver state-of-the-art translation, synthesis, and vulnerability repair, especially for low-resource domains like smart contracts. At the systems layer, I introduce DeFaaS, a blockchain-managed, multi-cloud Function-as-aService platform that removes single points of failure. I further prototype OGAIS, which enables trusted, on-device LLM inference triggered and verified by smart contracts, and I demonstrate zero-knowledge-proof workflows that preserve user privacy. To sustain performance, I repurpose Tensor Processing Units as cryptographic accelerators, cutting the latency of homomorphic encryption and zero-knowledge proofs by an order of magnitude. The resulting stack keeps every model invocation auditable while sensitive data stay encrypted. Together, these contributions advance AI-driven software engineering and establish a secure path for its deployment. By uniting reasoning-centric agents with verifiable, decentralized execution, this dissertation lays the groundwork for autonomous development tools that are demonstrably more accurate, transparent, and trustworthy
Reducing Stress in Math Tests Through Video-Based Question Formats: A Multimodal Affective Study
No full textFoundational math tests in early undergraduate education are often associated with stress and negative emotional responses, which can lead to avoidance of quantitative coursework and limit long-term academic and career opportunities. To address this issue, we introduce a novel type of math question framed as a relatable short video story. We tested the effectiveness of this design in an experiment with 50 participants who completed a math test that contained conventional and video-based questions of comparable difficulty. Throughout the test, we continuously recorded physiological indicators of arousal and observational indicators of emotional valence. These included facial perspiration via thermal imaging, heart rate and heart rate variability via smartwatches, and facial expressions via webcam. After calibrating responses to individual baselines and normalizing the data, mixed-effects models revealed that conventional questions elicited significantly higher arousal than video questions. In addition, video-based questions were more strongly associated with positive affective responses. Importantly, these psychophysiological benefits were achieved without compromising test performance. Using Machine Learning (ML), we also demonstrated that it is possible to predict with significant accuracy (~70\%) the correctness of student responses to questions in the foundational math test. However, this is primarily due to the existence of subsets of easy/difficult questions in the said test that are solved/not solved by nearly everybody. Together, our study provides empirical support for an affective redesign of foundational math assessments, one that aligns with the media consumption habits of today’s learners and is well suited to the YouTube era