My dissertation considers specific forms of authorship and the novel produced amid the changes to technology, reading, and audience/author relationships in the first two decades of the twenty-first century. Synthesizing several threads of analysis—including postcolonial approaches to twenty-first century novels, explorations of authorship in literary and media studies, and growing attention throughout literary studies to global anglophone as a conceptual framework—I examine the contemporary global anglophone novel form through analyses of four authors who have global biographies, international cultural capital, and a thematically and structurally global body of work. Through analyses of Chimamanda Ngozi Adichie, Kamila Shamsie, Zadie Smith, and Bernardine Evaristo’s novels, media coverage, and other writings, I highlight the ways the contemporary global anglophone novel reflects and responds to the changing sociohistorical and cultural contexts of the twenty-first century. I demonstrate that the paratextual function of an author, their brand or reputation, is a key node in determining the value and circulation of the global anglophone novel because it acts as a site of connection between global authors, readers, novels, and institutions. As the global anglophone endures and develops as a critical category, work is needed to better outline its contours. In this dissertation, I work to illustrate one possible approach to building an understanding of the global anglophone through attention to its theorizations and articulations in the literature and author figures it produces.

Objectives: Interproximal reduction is a technique used to reduce the mesiodistal dimension of teeth. Interproximal reduction is programmed into the Invisalign ClinCheck and should be reflected clinically. This study aimed to evaluate the initial, approved, and performed interproximal reduction to determine differences between what was planned and performed. Materials and Methods: 57 subjects had an initial and approved Invisalign ClinCheck over the study period. Patient number, dental arch, initial or approved Invisalign ClinCheck, interproximal location, and the amount of interproximal reduction were recorded. For 32 of 57 subjects with an additional intraoral scan prior to refinement, mesiodistal tooth width measurements were recorded using OrthoCAD software and digital caliper. Measurement difference between pre-treatment and refinement indicated the amount of interproximal reduction performed. Wilcoxon signed-rank tests were used to compare the initial, approved, and performed interproximal reduction. Results: There was a significant difference in interproximal reduction between the initial and approved Invisalign ClinCheck overall (p=0.00) and interproximal locations 6,15-19 (p<0.05), with an increased amount of interproximal reduction in the approved Invisalign ClinCheck according to median values. For those with an additional intraoral scan prior to refinement (n=32), there was a signficant difference in interproximal reduction between the initial and approved Invisalign ClinCheck for the maxillary arch (p=0.004). There was a signficant difference in interproximal reduction between the initial ClinCheck and measurement with a digital caliper for the maxillary and mandibular arches (p=0.02) with a median value of -0.1 mm. No other differences were found. The intra-examiner correlation was moderate (intraclass correlation = 0.589). Conclusions: There is significantly more interproximal reduction prescribed in the approved Invisalign ClinCheck than the initial ClinCheck, specifically between the maxillary central incisors and the mandibular incisors. However, there was no difference found between the initial ClinCheck interproximal reduction and performed interproximal reduction. This study found that significantly greater interproximal reduction is required than performed clinically.

Accurate modeling of structural ensembles is essential for understanding molecular function, predicting molecular interactions, refining molecular potentials, protein engineering, drug discovery, and more. Here, we enhance molecular modeling through Bayesian Inference of Conformational Populations (BICePs), a highly versatile algorithm for reweighting simulated ensembles with experimental data. By incorporating replica-averaging, improved likelihood functions to better address systematic errors, and adopting variational optimization schemes, the utility of this algorithm in the refinement and validation of both structural ensembles and empirical models is unmatched. Utilizing a set of diverse experimental measurements, including NOE distances, chemical shifts, and vicinal J-coupling constants, we evaluated nine force fields for simulating the mini-protein chignolin, highlighting BICePs’ capability to correctly identify folded conformations and perform objective model selection. Additionally, we demonstrate how BICePs automates the parameterization of molecular potentials and forward models—computational frameworks that generate observable quantities—while properly accounting for all sources of random and systematic error. By reconciling prior knowledge of structural ensembles with solution-based experimental observations, BICePs not only offers a robust approach for evaluating the predictive accuracy of molecular models but also shows significant promise for future applications in computational chemistry and biophysics.

The concept of “genuine hospitality” extends beyond the mere provision of tangible offerings and hospitable behaviors by the host. It requires true hospitableness on the part of the service providers themselves. However, like humans, can AI also serve as a provider capable of embodying hospitableness? This dissertation seeks to establish a comprehensive theoretical framework called the Consciousness Attribution Model of AI Hospitableness (CAMAH) which encompasses three interconnected aspects: (1) the mechanism of consciousness attribution by consumers towards AI-enabled service providers, (2) the necessity of such attributions in recognizing the symbolic value of AI hospitableness, and (3) a nuanced comparison between human and AI providers concerning their capacity to deliver genuine hospitability. Structured into three scholarly essays, this dissertation first undertakes a philosophical and conceptual exploration, culminating in the proposition of CAMAH. Extending the theoretical foundations established in Essay 1, the subsequent essays (2 and 3) delve into empirical investigations within specific service technology domains, focusing on service robots and AI avatars equipped with self-service technologies, respectively. The significance of this dissertation lies in its identification of a necessary condition for AI service providers to be recognized as hospitable hosts capable of imparting hospitality-oriented, symbolic value, while clearly delineating the key boundaries that distinguish AI service providers—notwithstanding their potential to equip with anthropomorphic behaviors/forms—to human counterparts.

Climate change is a critical issue, as emphasized by the latest Intergovernmental Panel on Climate Change report (2023). Business organizations significantly contribute to greenhouse gas emissions but also play a crucial role in developing decarbonization solutions. A surge in scholarly attention since the mid-2010s has provided valuable insights into the dynamic interplay between firms and climate change. Studies have quantified risks and assessed the impact of environmental practices, while others have examined proactive measures by firms in response to regulatory landscapes and stakeholder expectations. External stakeholders, including governments, shareholders, and business partners, play a pivotal role in steering firms toward low-carbon strategies. However, there remains a gap in understanding the true impact of firm strategies on ecosystem health – for example on firms’ carbon footprint. This research aims to explore the influence of various actors on firms' decarbonization strategies and explores how firms navigate their transition towards low carbon amid conflicting pressures from financial markets, governments, and corporate customers in global value chains. The first essay reviews the literature on the challenges faced by multinational companies (MNCs) when trying to implement more sustainable practices in their supply chains. The second essay empirically investigates MNCs' impact on their suppliers' environmental performance, highlighting the importance of scrutiny, enforcement and economic leverage. The third essay analyzes investor reactions to coal plant divestment announcements by U.S. electric utilities, revealing increasing investor support for divestment. Overall, this work contributes to the literature at the intersection between firms and the environment in a global transition context, by taking a multidisciplinary and integrative approach. It also offers valuable insights for managers and policymakers as it highlights the necessity to account for contextual dynamics (e.g., change in value among stakeholders), and the breadth of the issues at stake (e.g., greenhouse gas emissions are concentrated at the manufacturing stages) to design more efficient environmental strategies and policies.

As the Vietnam War extended into the 1970s, concerns arose in Washington about the decreased number of men enlisting in the armed services. Conscription kept the ranks full temporarily, but the draft’s end precipitated a crisis. Due to the increased need for humanpower, the military broke with precedent and disbanded its female auxiliary organizations, admitting women as full-fledged members. This dissertation explores the first twenty years of women’s service after integration, from 1972 (the year that the last draft calls were issued) to 1992 (just after the First Gulf War) to examine the experiences of American women in uniform and how they affected a gendered military structure. In doing so, it argues that servicewomen were seen as both “ladies” and “brothers.” It explores how these contradictory identities affected women’s military experiences, striving to tell this story in the voices of the women involved by drawing on previous interdisciplinary scholarship, supplemented by archival research and oral historiesWomen’s experiences in the United States military were inherently different than men’s. This dissertation seeks to determine how concepts of gender changed in the military, and how those changes impacted servicewomen’s experiences. Just as important is an assessment of how female veterans viewed their own experiences after they returned to civilian life. Sexual harassment and assault will loom large as examples of some of the gendered obstacles women faced. Since those two transgressions concern power, not sex, most of these incidents involve men exerting control over women. This dissertation therefore looks at the ways in which sexual harassment and assault affected the lives of servicewomen: how the military and the women themselves conceptualized their experiences as gendered or not. Despite the marked change in servicewomen’s status, the Defense Department maintained a policy that pretended there was no role for them in combat. The United States would rather cling to the fantasy that women had not served under fire than admit that they were in dangerous situations. This dissertation offers case studies that challenge the fiction that women did not enter combat until the 21st century. Beginning with the invasion of Grenada, women saw themselves as warriors in a combat zone, regardless of the military’s blinkered point of view. In exploring women’s service during the 1980s and the First Gulf War, I am contributing to the recent historiographical trend that challenges the idea of women as noncombatants. These women’s roles, in fact, blurred the line between combatant and noncombatant. Setting the creation of the All-Volunteer Force (AVF) in the context of liberalized women’s participation in the armed services, this dissertation explores the unappreciated changes that transformed the military during the 1970s, 1980s, and early 1990s. While the AVF marked the beginning of increased opportunities for women in the United States military, the backlash against women that occurred in the 1980s did not impact only civilian affairs. The military therefore reflected both positive and negative changes that swept the civilian world. This dissertation will assess how women navigated those changes and explore why they occurred by attempting to create a comprehensive historical narrative of women’s military experiences that traces the service and lives of military women from the end of Selective Service through their active involvement in the First Gulf War.

This dissertation explores the application of self-supervised learning methods in dental radiology to address the challenges posed by limited data availability for training deep learning models. The overarching goal is to enhance the efficiency and accuracy of automated systems for various dental diagnostic tasks, including teeth numbering, detection of dental restorations, orthodontic appliances, implant systems, marginal bone level, and dental caries from panoramic radiographs, CBCT images, intra-oral 3D scans, and dental radiographs. Key contributions include the development of several novel approaches: Self-supervised Learning for Dental Panoramic Radiographs: Utilizing SimMIM and UM-MAE with Swin Transformer, we achieved significant improvements in teeth detection and instance segmentation, increasing the average precision by 13.4% and 12.8%, respectively, over baseline methods. Self-Distillation Enhanced Self-supervised Learning (SD-SimMIM): Enhancing SimMIM with self-distillation loss, we improved performance on teeth numbering, dental restoration detection, and orthodontic appliance detection tasks, demonstrating superior outcomes compared to other methods. DentalMAE for Intra-oral 3D Scans: Extending the mesh masked autoencoder (MeshMAE), DentalMAE evaluates predicted deep embeddings of masked mesh triangles, yielding better generalization and higher accuracy in teeth segmentation tasks. DEMAE for Dental CBCT Images: Proposing the Deep Embedding MAE (DEMAE), which measures the closeness of predicted deep embeddings of masked patches to their originals, we achieved significant accuracy improvements in teeth segmentation from CBCT images. Masked Deep Embedding (MDE) for Implant Detection: By leveraging MIM, we developed MDE to enhance dental implant detection, creating a comprehensive Implant Design Dataset (IDD) with expert annotations, significantly boosting detection performance. Deep Embedding of Patches (DEP) for Bone Loss Assessment: An extension of MAE, DEP improved the accuracy of marginal bone level detection, supported by the creation of a Bone Loss Assessment Dataset (BLAD) with detailed annotations. Masked Deep Embedding of Patches (MDEP) for Caries Detection: This method enhanced dental caries detection performance, validated on the CariesXrays dataset, demonstrating higher precision and recall rates compared to traditional baselines. Through these innovations, the dissertation establishes the efficacy of self-supervised learning in overcoming data scarcity in dental imaging, offering promising AI-driven solutions for improved diagnostics and patient care in dentistry.

Global water scarcity has necessitated the development of new technologies to provide clean and reliable water for the future. Current global infrastructure is insufficient to meet projected demand, and technologies that can provide efficient and low-cost water are urgently needed. Among the various water treatment methods developed over the years, adsorption has become a cost effective, easy to operate, and reliable method for water treatment. Nanoadsorption has emerged recently as an extension of traditional adsorption by combining the tried-and-true adsorption principles with unique material properties such as fast kinetics, large surface areas, and contaminant selectivity that can be used to remove a variety of contaminants. Unfortunately, these new adsorbents cannot be used in traditional adsorption settings such as columns and flow through systems because they cause high pressure drops, have poor mechanical strength, and are difficult to separate from water. In application, nanoadsorbents generally have been dispersed in water or embedded in macroscale hierarchical structures, but the risk of releasing contaminant containing nanoparticles into treated water necessitates a recovery or retention system. Nano enhanced dialytic purification involves utilizing a dialytic purification system that employs a membrane to separate a suspension of continuously recirculated nanomaterials from a stagnant solution or counterflowing stream of contaminated water. Contaminants diffuse down their concentration gradient, through the membrane, and into the nanomaterial suspension to be adsorbed. The nanomaterials are retained behind the membrane and act as a continual sink for the contaminants. This process was first exemplified in proof-of-concept experiments using a dialyzer with a single tubular membrane. Stagnant solutions of arsenic or lead were added into the lumen of the membrane and dialyzed using a flowing stream of ferrihydrite or hexagonal birnessite nanoparticles, respectively, over a three hour period. A greater than 90% removal was obtained from both experiments at sufficiently high adsorbent loading. The dialytic experiment between arsenic and ferrihydrite was compared to batch adsorption studies with a 94% removal efficiency at similar adsorbent loading. The dialytic experiment between lead and hexagonal birnessite was developed into a 2D axisymmetric CFD model using COMSOL Multiphysics® to study the process mathematically, and computational results were in good agreement with experimental data. The computational model was then extended to feature (for the first time) a 3D dialyzer with multiple working hollow fibers, counterflowing contaminant and adsorbent streams, and two treatment modalities – single-pass and multi-pass. These mass exchangers feature a larger surface area and improved diffusions rates over the single-fiber tubular membrane dialyzer. Methylene blue (MB) and powder activated carbon were used as the model contaminant and adsorbent. The computational model explored key parameters of the dialytic purification process and provided insight into the impact of parameter values on the overall removal of MB. Through these efforts, the dialytic purification process was successfully described mathematically, and the model can be used to explore real batch water treatment or pump and treat remediation applications to provide clean water for the future. The idea of utilizing nanoadsorbents retained behind a membrane to facilitate contaminant removal in a mass exchanger can also benefit analogous fields utilizing similar dialytic processes such as hemodialysis in the medical field and carbon dioxide removal in the petrochemical industry.

Chemosynthetic ecosystems in the deep sea hold vast amounts of untapped energy that until recent decades have been largely unobtainable. With the growing demand for resources and constant advancements in technology, these ecosystems and the diverse communities that inhabit them now face increasing pressure from anthropogenic exploitation activities. Thus, employing effective management and conservation strategies to avoid devastating these long-lived communities is imperative. However, effective protection hinges on a thorough understanding of these ecosystems. Here, I present a number of studies conducted on methane seeps along the Pacific Costa Rican Margin (CRM), exploring various ecological dynamics and highlighting the unique biodiversity thriving there. These studies aim to address gaps in our knowledge regarding the “sphere of influence” surrounding chemosynthetic methane seeps, providing insights into the flow of energy within these ecosystems, their spatial dynamics and how they interact with background deep-sea habitats. In Chapter 2, I employ a novel seascape approach using systematic surveys of several actively seeping areas to characterize the seep communities and delineate distinct seep zones, testing for inter- and intraspecific differences in community structure. Our results reveal nuanced patterns in α and β diversity between sites and across different zones, driven largely by depth. Additionally, I identify transitional zones extending the spatial extent of the seeps by up to 300 meters, emphasizing the “sphere of influence” surrounding these ecosystems.

This dissertation was designed to explore how selected principals enrolled in the National College for Educational Leadership (NCEL) Emotional Intelligence training program perceive whether the training has influenced their practice. While serving in various capacities as an educational professional, I have developed a strong appreciation for the significance of effective leadership in ensuring quality education is achieved. This dissertation is based on using emotional intelligence theoretically to explore the constructs of effective leadership, establish a model for understanding leadership, and create a program to support the systematic development of educational leaders. This study uses a qualitative research design, employing interviews as the main data collection method. The research sample comprises ten participants (all principals are located in the small Caribbean Island of Jamaica). The approach employed by the researcher to analyze the data was the thematic analysis method, which identifies the common insights and themes exploring the participants' perception of the National College for Educational Leadership, Emotional Intelligence training module. The primary research question explored whether K-12 principals perceive emotional intelligence as useful for improving their leadership. The study's findings indicated that participants found the emotional intelligence training program valuable and believed it added value to the quality of their leadership. The principals that participated in the research reported specific behavioral changes attributable to their participation in the emotional intelligence training. The research presents a nuanced exploration of local principals and their perception of the emotional intelligence training they participated in. The study explores how emotional intelligence training adds value to their practice as educational leaders to effect educational transformation in their schools. The research also presents tangible recommendations for policymakers to improve the emotional intelligence training program.