EVALUATION AND FIELD VERIFICATION OF STRENGTH AND STRUCTURAL IMPROVEMENT OF CHEMICALLY STABILIZED SUBGRADE SOIL (FHWA-OK-08-01 2195)

Often subgrade soils exhibit properties, particularly strength and/or volume change properties that limit their performance as a support element for pavements. Typical problems include shrink-swell, settlement, collapse, erosion or simply insufficient strength. A common approach to subgrade soil support or stability problems involves chemical modification or stabilization with additives such as lime (hydrated or quick), fly ash (Class C from lignite coal), cement kiln dust (CKD) or Portland cement. Other additives are available, but this group constitutes the major products or by-products used on roadway construction in Oklahoma. The type and amount of chemical additive is dependent on the purpose or function of the treated material (i.e., improved physical properties or improved strength) and selection is based on accepted or standardized procedures. Questions then arise with regard to chemically treated subgrade soils about the rate of development and ultimate value of improvement. The purpose of this research is to develop relationships between rate of development and magnitude of strength (or physical property) improvement for chemically treated subgrade soils. The research project involved laboratory and field studies of the influence of cementitious additives on the strength and structural improvement of stabilized subgrade soils. Laboratory tests for measuring strength and structural improvement (e.g. UCS and MR) were conducted on field mixed treated soils and laboratory mixed treated and untreated soil samples. UCS and MR tests were conducted on samples varying curing time (field and laboratory mixed) and percent additive used (laboratory mixed). A series of field tests (Nuclear w-?, stiffness gauge, portable FWD, Dynamic Cone Pentrometer, and PANDA Pentrometer) were conducted at five field test sites on the untreated subgrade soils and on the treated subgrade soil with curing time as allowed by the construction schedule. The research project collected a large volume of both laboratory and field data which are summarized in the appendixes (5) to this report.Final Report October 2006-September 2008N

Daughter of another

The fantasy genre has always been a way to explore and express the imagination and worlds only dreamt of. This project examines methods of writing fantasy from a decolonized perspective and what that looks like, comparing existing fantasy anthologies and novels to those that are currently in the mainstream but still perpetuates the colonial and white-supremacist mindset i.e. "A Court of Thorns and Roses" by Sara J Maas. This novel, Daughter of Another, uses the mythos and tales from marginalized communities across the United States, similar to "Fire Keeper’s Daughter" by Angeline Bouley. This project navigates the best way to approach such writing style within the fantasy genre and how an outsider can speak on subjects, communities, and cultures in a way that is objective and amplifies these marginalized voices rather than drowning them out. Through the lenses of decolonization and intersectionality, Daughter of Another follows Hiraya’s odyssey in a parallel realm to Earth where colonization never occurred and magic and myth still persist. She travels across Keya Island in search of her long-lost half-sister, hoping to save her city from the next heir: her immature younger half-brother. In doing so, she is drawn into a journey interwoven with myths and stories not of her own. The importance of this project is to understand there are worlds beyond those approved by a colonized lens when creating a fantastical world, and that it is crucial to involve a diverse array of groups—especially marginalized communities—that do not support colonial powers, but instead dismantle them and amplify these subjugated voices

INVESTIGATION OF STRATEGIES FOR IMPROVING THE PERFORMANCE OF SMART THERMOSTAT-DRIVEN FDD METHODS

This dissertation presents an investigation of two strategies for evaluating the performance of smart thermostat-driven fault detection and diagnosis (FDD) methods that are intended for vapor compression air conditioning (AC) systems in residential homes. Though there have been some research efforts within the last decade on smart thermostat-driven FDD, there are several challenges which have limited the advancement of the concept into a marketable technology. Therefore, the investigation in this dissertation is to explore some of the ways these challenges can be addressed.For the first strategy, a novel dynamic co-simulation model is developed using EnergyPlus and Modelica to gain a fundamental understanding of the coupling between building thermal response and AC system. The model is then validated and used to investigate the effectiveness of using smart thermostats for low-cost FDD in residential AC systems. Smart thermostats mea¬sures indoor air conditions which reflect the indoor air responses to both AC operations and other factors like weather and building gains. As these other factors are typically unmeasurable by smart thermostats and yet prone to variability, they pose potential disturbances which can affect AC operation. This makes estimating the impact of these disturbances (referred to as uncontrollable building load disturbances (UBLDs)) on AC operation in buildings and the performance of FDD methods critical. Therefore, in this study, it is essential to couple the dynamics of AC operation and building response in the development of the co-simulation model. Additionally, a novel automated calibration framework is also developed in this research to validate the coupled model. The validated model was then used to simulate seven UBLD cases and two prevalent faults with different severities. The study also proposed a set of metrics for evaluating the performance of FDD methods. With the simulation results and the proposed metrics, impact analysis on AC duty factor and enthalpy change were carried out. Results of the analyses showed that severe UBLDs can cause almost 9% increase in energy consumption. The results also showed that these UBLDs can cause false alarm rates in FDD up to 70% if appropriate thresholds and post-processing strategies are not used. Meanwhile, results on the impact of faults showed that with the simultaneous impact of UBLDs, only low charge faults up to 30% severity and low indoor airflow fault up to 60% severity can be confidently detected using duty factor as FDD feature. Meanwhile, the analyses and results also led to the proposal of another FDD feature (enthalpy change) which offers better sensitivity and a potential for diagnosis which the duty factor feature lacks. With this new feature, an automated FDD algorithm was developed, validated and successfully deployed in four test-homes where it was able to detect 30% undercharge fault and 30% low indoor airflow, as well as installation mismatch. Overall, this research work creates a new pathway for promoting smart thermostat FDD technology in the residential market within the US and beyond

Unions, Race, and the Uneven Rewards of Teaching

This study examines how racial income inequality persists within the teaching profession, a field often considered a pathway to stable, middle-class employment due to its standardized pay structures and strong union presence. This makes teaching a particularly revealing case for examining wage stratification within a predominantly public-sector and highly unionized occupation. Public-sector jobs are often noted for their government oversight, and unions are also recognized for promoting pay equity among union members. However, both systems operate within larger structures of inequality, which can limit their effectiveness in addressing wage disparities. By investigating the intersection of race and ethnicity, union membership, and wages, this research uncovers several mechanisms that disrupt or perpetuate wage disparities among teachers. This study uses data from the Current Population Survey Merged Outgoing Rotation Group (CPS MORG) from 1989 to 2023 to address the question: how does union membership intersect with race to influence wages among teachers? The analysis highlights the dual role unions play in disrupting and perpetuating wage disparities, revealing a nuanced relationship between union membership, race and ethnicity, and wages within the teaching profession. This underscores the complexities of labor stratification even in regulated environments. The teaching profession illustrates how institutional mechanisms like unionization interact with broader patterns of inequality, sometimes reproducing disparities they aim to dismantle. By focusing on the intersection of union membership and racial inequality in a public-sector profession, this study adds nuance to current debates on labor equity. It highlights how unions can both alleviate and reproduce disparities, depending on the broader institutional and historical contexts in which they operate

METHANE MITIGATION IN NATURAL GAS INDUSTRIAL ENGINES: HYDROGEN FUEL BLENDING AND THE INTEGRATION OF CERAMIC ELECTROCHEMICAL REACTORS

Methane emissions are one of the primary emissions causing global warming. Natural gas-fed industrial engines are one of the key drivers in excessive methane emissions due to partial load operations and combustion inefficiencies that can be controlled with modern methane mitigation strategies. In this thesis engine-based and post-combustion methane mitigation strategies are analyzed including spark ignition timing, hydrogen blending, and post-combustion protonic ceramic electrochemical cells (PCERs). The spark ignition timing is used to control the ignition time of the fuel and the electrochemical cells are considered for the conversion of exhaust methane into hydrogen. Hydrogen blending into natural gas is studied at 0%, 10%, and 20% hydrogen mole fraction and an engine load of 60%. The hydrogen mole fraction is calculated using an Aspen HYSYS model of the engine. The addition of hydrogen into the natural gas stream has resulted in a methane emission reduction of 16.2% and 35.2%. Modification of the ignition spark timing is conducted for the angles of 9°BTDC, 11°BTDC, 11.5°BTDC, and 12°BTDC, with the baseline angle being 11°BTDC. A significant methane emission reduction of 56.4% and 60.9% is observed at 11.5°BTDC, and 12°BTDC respectively due to an increase in combustion efficiency. The hydrogen blending and spark ignition are combined to obtain a methane reduction of up to 73% by improving thermal efficiency, promoting flame propagation, and reducing the methane content in the feed fuel. To remove all of the exhausted methane and convert CO to CO2 while obtaining a high-value chemical, an electrochemical cell is proposed as a post-combustion catalyst. The PCER model utilized a tubular, direct pass construction without an internal support tube. The model was constructed in engineering equation solver (EES) using a mole and electrochemical balance using a Ni/CeO2 anode, Ni BCZY electrolyte, and Ni/BCZY cathode. The PCER model was able to obtain complete methane conversion when operating at 2,000-5,000 A/m2 and a temperature between 673-780K when being fueled with realistic engine emissions. The technology developed here shows promising solutions for reducing and mitigating methane emissions from industrial engines and combustion devices

Essays in Corporate Governance and ESG

Corporate governance plays a crucial role in shaping firms’ strategic priorities, particularly in the realm of environmental, social, and governance (ESG) responsibilities. As firms face increasing pressure from investors, regulators, and stakeholders to incorporate sustainability into their business models, the effectiveness of ESG governance mechanisms has become a critical research question. My dissertation explores this issue through two interrelated studies that examine the role of board directors’ ESG expertise (Chapter 1) and sustainability-linked executive compensation (Chapter 2) in influencing corporate ESG outcomes.Chapter 1 investigates whether the ESG-related skill sets of board directors enhance firms’ ESG performance. Based on a main dataset of S&P 1500 firms from 2009 to 2022, I analyze whether directors with ESG expertise influence corporate ESG outcomes, particularly through their role in shaping CEO compensation structures. While prior literature suggests that directors’ expertise can improve firm performance, I find no consistent evidence that directors with ESG skill sets enhance corporate ESG performance. Instead, my findings reveal a potential ESG-washing phenomenon—where firms that appoint ESG-skilled directors appear to signal commitment to sustainability but fail to translate these governance changes into meaningful ESG improvements. However, I do find that directors with ESG expertise increase the likelihood of ESG targets being incorporated into CEO compensation contracts, particularly in S&P 500 firms, suggesting that these directors influence the formalization of ESG-related incentives at the executive level. Chapter 2 extends the analysis to examine whether sustainability-linked executive compensation effectively drives improved ESG outcomes. This study focuses on Fortune 250 firms, particularly those in the oil and gas sector, and explores how firms structure sustainability goals in CEO annual incentive plans (AIPs). I find that while sustainability-linked compensation is relatively uncommon—appearing in only 8% of firms as recently as 2020—it is most prevalent in high-polluting industries such as oil and gas. My analysis reveals that sustainability-linked incentive plans only improve environmental outcomes (e.g., CO2 emissions and regulatory penalties) for firms with a history of high pollution. However, for firms without a prior record of environmental misconduct, these incentives show no significant effect on sustainability performance. This suggests that ESG-linked CEO pay may function as a corrective measure for firms with poor environmental track records, rather than a proactive tool for enhancing corporate sustainability across all firms

PULSED LASER DEPOSITION AND OPTIMIZATION OF LSGM AND LSCF THIN FILMS FOR SOLID OXIDE FUEL CELL APPLICATIONS

The performance and durability of solid oxide fuel cells (SOFCs) are critically governed by the structural and chemical coherency of the hetero-interfaces between oxygen ion-conducting electrolytes and electronically conducting electrodes. In particular, realizing coherent and chemically stable interfaces between La₀.₈Sr₀.₂Ga₀.₈Mg₀.₂O₃−δ (LSGM), a fast oxygen-ion conductor, and (La₀.₆Sr₀.₄)₀.₉₅Co₀.₂Fe₀.₈O₃−δ (LSCF), a mixed ionic-electronic conductor, is critical for minimizing interfacial resistance and enabling high-efficiency energy conversion.A major challenge in solid-state fuel cells arises from the potential interdiffusion of cations across electrolyte/electrode interfaces, which can lead to the formation of undesired secondary phases and degraded ionic/electronic transport properties. Therefore, precise interface engineering is essential to preserve the functional stability of both layers. Despite the technological importance, detailed experimental studies remain lacking, especially on the epitaxial growth and hetero-interface structure of LSGM and LSCF heterostructures using Pulsed Laser Deposition (PLD). In this work, we systematically optimized PLD growth conditions for single-crystalline LSGM and LSCF thin films on SrTiO₃ (001) substrates, aiming to establish a coherent and well-defined hetero-interface suitable for fundamental studies of interfacial phenomena under the fuel cell operating condition. X-ray diffraction (XRD) analysis revealed that LSGM films grown at 800 °C and 150 mTorr of oxygen partial pressure exhibited a lattice parameter of 3.909 Å (0.017) with a full-width-at-half-maximum (FWHM) of 0.169°, while LSCF films grown under optimized conditions showed a lattice parameter of 3.916 Å (0.021) and an FWHM of 0.072°, indicating high crystallinity and epitaxial alignment. As a result, the successful fabrication of LSGM/LSCF heterostructure exhibited distinct XRD peaks from both layers, suggesting layered epitaxial growth without significant interdiffusion or secondary phase formation under the optimized conditions. This study provides a reproducible route for the coherent integration of LSGM electrolytes and LSCF electrodes via PLD, offering a model platform for exploring interfacial transport, defect chemistry, and long-term stability — essential aspects for the development of next-generation, high-performance SOFC devices

Predicting Kidney Post-Transplantation Function from Optical Coherence Tomography Images Using Machine Learning Approaches

Delayed graft function (DGF) is a major post-transplant complication in kidneyrecipients, particularly from deceased donors. This study investigates the effectiveness of machine learning approaches in predicting DGF using texture features extracted from optical coherence tomography (OCT) images, supplemented with Kidney Donor Profile Index (KDPI) scores. To address significant class imbalance between immediate graft function (IGF) and DGF, three strategies were evaluated: threshold optimization using the GHOST algorithm, data balancing via SMOTE-Tomek, and cost-sensitive learning (CSL). We demonstrate that classifiers trained on KDPI scores alone under- performed compared to those trained OCT images derived texture features. Classifiers trained on KDPI alone showed moderate improvements with GHOST, but incorporat- ing OCT-derived texture features significantly enhanced model performance across all classifiers. These findings underscore the utility of OCT imaging in assessing kidney allograft quality and predicting post-transplant outcomes, highlighting the potential of machine learning classifiers to estimate the risk of delayed graft function (DGF) in deceased donor kidneys prior to transplantation

Smart Policy Design for COVID-19: A Deep Reinforcement Learning Framework

More than 700 million people became infected and about 7 million have died from COVID-19, making it one of the deadliest pandemics in history. It triggered severe economic recession around the world that disrupted multiple industries such as agriculture, manufacturing and tourism. Governments around the world responded by implementing interventions to control disease transmission, but their impact varied from one country to another. There have been numerous studies to understand the effectiveness of the interventions but there are considerable variations in the interpretation. However, it is evident that tailoring policies for individual regions makes them more impactful. In the research, we decided to focus on regions of the United States and treat each state individually because of their diversity. We employ deep reinforcement learning to handle the dynamic nature of the disease, specifically multi-agent reinforcement learning where individual agent handles distinct states in a shared space to mimic realistic environment. The agent intervention differed from the actual intervention most of the time. Overall suggestion is to implement intervention earlier with higher intensity then gradually reduce aggressiveness and maintain a moderate level of intensity. However, this will depend on how much we want to prioritize public health over national economy