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Evidence for the Scandinavian Influence on the Phonology of Northern English Using Modern Dialect Data
No full textNorthern English refers to a British English dialect group spoken in the counties of Northumberland, Cumbria, Durham, Westmorland, Yorkshire, Lancashire, Cheshire, Manchester, Lincolnshire, Merseyside, and Tyne & Wear. During the times of Old English and Middle English, this area was referred to as Northumbria and was controlled and inhabited primarily by Scandinavian settlers who spoke Old Norse. This contact with the Norse resulted in many linguistic changes in northern English, with the greatest contribution traditionally argued to be the wealth of borrowings from the Old Norse lexicon. However, some scholars have briefly broached the topic of Scandinavian phonological influence in the region, primarily discussing the cluster /sk/.
To elucidate the phonological effects of Old Norse on Northern English, this thesis analyzes the development of certain aspects which point to Scandinavian influence found in the northern sound system from the times of Old English to the dialects of the modern day. In particular, I focus on the preservation of velar plosives, the cluster /sk/, initial voiceless glottal and labial-velar fricatives, final rhotics, and the development of PGmc */ai/, */oː, o/, and */uː/. Given that this influence took place during a time from which we have very few surviving texts from the northern region, dialect data is provided to supplant this deficit and to demonstrate earlier stages of language development.
While much of what makes the phonological inventory of Northern English archaic was once common to all Old English, the southern dialects of modern English have since innovated bringing them further from that of the modern Scandinavian languages. The aim of this research is to provide evidence of the Old Norse-Northern English phonological connection, as well as to shed light on the no-influence, koineization, and creolization hypotheses put forth by prior scholars in relation to the role of Old Norse contact in the development of the Northern English sound system
Autoregressive Modeling of DNA Molecule Shapes Accompanied by an Empirical Assessment of the Ljung-Box Test
No full textThe assumption of independence rarely holds in real-world data. Correlated observations are ubiquitous, especially in sequential contexts where time series models are essential for capturing temporal dependence. This study analyzed six groups of damaged and undamaged DNA sequences, where an F in the middle of a sequence indicates damage. One biological aim is to understand how DNA regenerates with the assistance of proteins that recognize damaged regions. Motivated by empirical support for AR(2) modeling, we fit autoregressive models to the first three principal component scores of each DNA group, capturing the dominant structure in the data. We conducted model diagnostics, estimated parameters via both MCMC and MLE and produced plots to evaluate model fit and assess proximity to nonstationarity. To streamline interpretation, we selected AFA and AGA to illustrate typical trends. Additionally, we investigated the behavior of the Ljung-Box test for residual autocorrelation. Our custom implementation, supported by a simulation study, yielded results consistent with the sarima() function in the astsa package, with both methods relying on the Ljung-Box test to assess model adequacy. Our approach also corrects inaccuracies in R’s default tsdiag() output. Under the null hypothesis, the simulated p-values followed a continuous Uniform distribution, with sample means and variances closely matching theoretical expectations. Power analysis further demonstrated that the Ljung-Box test becomes increasingly sensitive to residual autocorrelation as the omitted parameter ϕ(2) increases, clearly indicating model misspecification and underscoring the importance of including all relevant autoregressive components
The Impact of Social Emotional Learning (SEL) Lessons on Students’ Mental Health Surrounding Standardized Testing
No full textThis qualitative action research examined the emotional impacts of standardized testing on 4th and 5th graders and the effects of targeted social-emotional learning lessons in a Title 1 school. Pre- and post-intervention data were gathered through surveys (Children’s Test Anxiety Scale) and focus group interviews. By employing thematic and narrative analysis, findings revealed significant initial cognitive anxiety, physical symptoms, and difficulties in self-regulation. Following the SEL intervention, the data indicated that students exhibited improved emotional resilience, reduced anxiety symptoms, and enhanced coping skills. Although the SEL interventions enhanced overall emotional well-being, anxiety was not fully eliminated. This study highlights the effectiveness of targeted SEL programs in mitigating the adverse effects of standardized testing and underscores the necessity of integrating SEL into the curriculum while also calling for broader changes in testing practices
Synthesis of Inorganic Actinide Compounds with Neptunium, Plutonium, and Americium
No full textThere is a rush of countries around the world making the switch to greener energy sources. Nuclear energy power plants are an integral part of the shift from fossil fuels, but there are concerns over the best way to handle nuclear waste. Most of the nuclear waste is currently disposed of by incorporating the waste into a glass form through vitrification, but research into alternative waste forms, such as promising crystalline materials, could provide a more robust method of containment. Research on actinide materials is lacking due to the dangers of working with radioactive material and there are few labs capable of this type of research, which has led to a deficiency of known compounds containing transuranic elements. In order to circumvent the dangers of radioactive particles, research is often done using surrogate elements that are either nonradioactive or have low radioactivity, but have similar properties to the more radioactive actinides, such as using cerium as a surrogate for plutonium. Research done using surrogates is helpful, but it is important to extend the knowledge gained to the actual actinide elements to study their fundamental properties.
This dissertation focuses on inorganic crystal structures formed with plutonium, neptunium, and americium and the structural properties of those materials. These structures have been synthesized using mild hydrothermal and molten flux techniques. Properties such as ionic radii, oxidation states, preferred coordination environment, and similarities to their surrogate analogs have been gleaned from the study of these structures
Elevating Next Generation Wireless Devices Towards Contactless Sensing for Healthcare Applications
No full textThere is an increasing interest in technologies that can understand and perceive at-home human activities to provide personalized healthcare monitoring, aimed at early detection of disease markers and assisting physicians in making clinical decisions. Existing approaches, such as wearables, require users to wear sensors that can be cumbersome and cause discomfort. Vision based solutions, such as optical cameras, IRs, LiDARs, etc., can be used to design contactless at-home monitoring systems. However, these systems are limited by poor lighting and occlusion, and they are privacy-invasive. Fortunately, high-frequency millimeter-wave wireless devices provide an effective alternative to the existing systems to enable fine-grained health monitoring: Millimeter-wave signals can penetrate certain obstacles, work under zero visibility, and have higher resolution than Wi-Fi. Further, major network providers are actively deploying millimeter-wave technology, a core component of next-generation wireless networks, in both large-scale networks and home routers, thereby paving the way for its widespread adoption in 5G and future devices. This opens up a new opportunity for at-home contactless sensing. But, the eventual success of using millimeter-wave technology for sensing depends on system designs that address the unique challenges of millimeter-wave signals: specularity, variable reflectivity, and low resolution. These issues can lead to incomplete and noisy information about the human subject in the reflected signals, making it difficult to directly estimate human related information. However, these reflected signals exhibit correlations with various human activities and carry distinct signatures, allowing for the use of data-driven learning models to deduce useful information.
In this dissertation, we develop data-driven deep learning models to address the fundamental challenges of millimeter-wave sensing. We first design and evaluate deep learning models based on conditional Generative Adversarial Networks to estimate the posture of a person by generating high-resolution human silhouettes and predicting 3D locations of body joints. We then extend the sensing capabilities to enable contactless sleep monitoring, classifying sleeping states, and predicting sleep postures. Furthermore, we facilitate contactless lung function monitoring by combining wireless signal processing with deep learning, enabling a software-only solution for at-home spirometry tests. Finally, we demonstrate the clinical utility of millimeter-wave sensing through two real-world deployments: a contactless cardiac monitoring system for stroke patients that estimates heart rate and heart rate variability; and a bed event detection system deployed in hospitals for 24-hour monitoring of high-fall-risk patients, aiming to enable timely interventions and prevent inpatient falls. Together, these systems demonstrate the potential of millimeter-wave sensing to elevate next-generation wireless devices into scalable, privacy-preserving platforms for contactless health monitoring across both home and clinical settings
Investigating Mechanisms and Day-Level Factors That Influence Children’s 24-Hour Movement Behaviors
No full textTwenty-four hour movement behaviors (i.e., physical activity (PA), screen time (ST), and sleep) are associated with health outcomes, chronic disease (obesity, type 2 diabetes), and mortality. Most literature evaluating children’s 24-hour movement behaviors has focused on static person-level demographic information such as age or biological sex. Relatively less work has examined the variable factors that change day to day, such as environment or context. The Structured Days Hypothesis (SDH) suggests that when children are in structured environments, they have healthier movement behaviors compared to less structured environments where children have more discretionary time to engage in obesogenic behaviors. Besides school/childcare, out-of-school programs are the most common form of structured environments for children. According to the America After 3PM, children from low-income households are less likely to attend out-of-school programs, and one of the primary barriers to attend out-of-school programs is the cost. Therefore, the relationship between income and 24-hour movement behaviors may be explained, at least partially, by time spent in out-of-school programs.
Furthermore, our understanding of 24-hour movement behaviors in children is limited to person-level correlates which does little to provide information about a child’s movement behaviors on any given day. Previous research has relied on aggregated data rather than examining the natural variability in 24-hour movement behaviors and subsequent related factors at the day-level. Using aggregated data assumes that all days are homogeneous; however, we know that children’s activities and behaviors are variable across days.
Examining the day-level variability of contextual factors (i.e., childcare and out-of-school programs) may be able to shed light on potential mechanisms influencing children’s 24-hour movement behaviors more directly. This information will inform intervention strategies aimed to leverage already-existing programs to improve children’s 24-hour movement behaviors. The purpose of this dissertation is to use the SDH to examine the relationship between daily participation in out-of-school programs and children’s 24-hour movement behaviors, and to examine how structure may be mediating the relationship between household income and children’s 24-hour movement behaviors
A Foundation for Predictive Process Control Digital Twins in Smart Manufacturing and Industry 4.0
No full textMore than 50 years of sustained manufacturing decline in the United States has plagued the availability of quality goods and employment, thereby eroding the social health and quality of life of American families. Smart manufacturing and industry 4.0 aim to reverse this trend and revolutionize manufacturing by leveraging emerging technologies such as artificial intelligence and digital twins to improve output, productivity, and opportunity.
Digital twins have emerged as a key enabling technology for smart manufacturing and industry 4.0, garnering significant attention, prioritization, and investment. Despite their popularity, concrete implementations of digital twins are scarce and lack the key capabilities of prediction and control in combination. Leveraging artificial intelligence\u27s predictive capability, this dissertation examines the barriers which inhibit the implementation of digital twins in smart manufacturing systems and contributes to laying a foundation for establishing predictive process control digital twins including their fundamental building blocks of data acquisition, testbed access, and time-series analytics.
In addition to providing technical analyses which enable researchers and practitioners to overcome system integration and testbed capability challenges, this dissertation posits that interoperability is an underestimated and pervasive challenge on the factory floor. Furthermore, this dissertation posits that interoperability inhibitors are as much social as they are technical, and the failure to achieve robust and widespread interoperability is due to an overemphasis on technical in lieu of social influencing factors. In addition, this dissertation identifies limited interdisciplinary collaboration and limited access to capable testbeds which provide streaming authentic manufacturing data as major inhibitors to emerging technology research and implementation. To overcome these inhibitors, this dissertation proposes a novel method for providing worldwide access to commissioned testbeds and achieving geographically-distributed collaboration. Furthermore, this dissertation demystifies time-series analytics in manufacturing and empirically evaluates classification and forecasting algorithms, providing practical guidance for algorithm selection and implementation. Finally, this dissertation evaluates time-series classification performance, industrial communication performance, and the tradeoffs between edge computing and computational offloading in the context of predictive process control, laying a foundation for the realization of predictive process control digital twins.
Through providing deep technical analyses of technologies, methods, solutions, and tradeoffs for practitioners and researchers coupled with educational resources and tools for educators and autodidacts, this dissertation aims to facilitate the implementation of emerging technologies on the factory floor and in research labs while expediting the upskilling and bootstrapping of the manufacturing workforce. Embracing the American spirit of innovation and invigorating the U.S. manufacturing workforce\u27s potential to fashion dignity and prosperity, this dissertation aims to lay a foundation for addressing critical social and technical manufacturing challenges in order to furnish the quality goods and employment needed to improve the social health and quality of life for American families
Hearing the Unheard: Creating a Brave Space to Analyze the Experiences of Men of Color in Student Service Roles and Their Mental Health Challenges Within Higher Education
No full textThis study analyzed the experiences of five men of color in student service roles dealing with mental health stressors, such as discrimination, workplace burnout, tokenism, and low morale. To highlight their experiences, a brave space was utilized as an intervention. The following questions were examined and answered within this qualitative, action research study: (1) What are the experiences of men of color with mental health challenges in student service roles? (2) How do mental health problems affect the personal and professional lives of men of color in student service roles? (3) How do the brave spaces impact the mental health and professional well-being of men of color in student service roles? Critical Race Theory (CRT), Gender Role Theory, and Sense of Community Theory were employed as a theoretical framework to guide the study. Based on the narrative approach, three major themes that emerged were understanding the impact of systemic issues and institutional policies, recognizing emotional barriers, and understanding the importance of student advocacy. These major themes, along with other findings in the study, inform recommendations, limitations, and future studies
A Neuro-Symbolic AI Approach to Scene Understanding in Autonomous Systems
No full textEffectively understanding scenes requires a unified representation of scene data and background knowledge. A neuro-symbolic AI approach to scene understanding leverages such a unified representation to enable advanced expression, inference, and labeling of scenes, improving the perception of autonomous systems.
Scene understanding remains a central challenge in the machine perception of autonomous systems. It requires the integration of multiple sources of information, background knowledge, and heterogeneous sensor data to perceive, interpret, and reason about both physical and semantic aspects of dynamic environments. Current approaches to scene understanding primarily rely on computer vision and deep learning models that operate directly on raw sensor data to perform tasks such as object detection, recognition, and localization. However, in real-world domains – such as autonomous driving and smart manufacturing/ Industry 4.0 – this sole reliance on raw perceptual data exposes limitations in safety, robustness, generalization, and explainability. To address these challenges, this dissertation proposes a novel perspective on scene understanding using a Neuro-symbolic AI approach that combines knowledge representation, representation learning, and reasoning to advance cognitive and visual reasoning in autonomous systems.
Our approach involves several key contributions. First, we introduce methods for constructing unified knowledge representations that integrate scene data with background knowledge. This includes the development of a dataset-agnostic scene ontology and the construction of knowledge graphs (KGs) to represent multimodal data from autonomous systems. Specifically, we introduce DSceneKG, a suite of large-scale KGs representing real-world driving scenes across multiple autonomous driving datasets. DSceneKG has already been utilized in several emerging neuro-symbolic AI tasks, including explainable scene clustering and causal reasoning, and has been adopted for an industrial cross-modal retrieval task. Second, we propose methods to enhance the expressiveness of scene knowledge in sub-symbolic representations to support downstream learning tasks that rely on high-quality translation of KG into embedding space. Our investigation identifies effective KG patterns and structures that enhance the semantic richness of KG embeddings, thereby improving model reasoning capabilities. Third, we introduce knowledge-based entity prediction (KEP), a novel cognitive visual reasoning task that leverages relational knowledge in KGs to predict entities that are not directly observed but are likely to exist given the scene context. Using two high-quality autonomous driving datasets, we evaluate the effectiveness of this approach in predicting entities that are likely to be seen given the current scene context. Fourth, we present CLUE, a context-based method for labeling unobserved entities, designed to improve annotation quality in existing multimodal datasets by incorporating contextual knowledge of entities that may be missing due to perceptual failures. Finally, by integrating these contributions, we introduce CUEBench, a benchmark for cognitive visual reasoning that systematically evaluates both neuro-symbolic and foundation model-based approaches (i.e., large language models and multimodal language models). CUEBench fills a critical gap in current benchmarking by targeting high-level cognitive reasoning under perceptual incompleteness, reflecting real-world challenges faced by autonomous systems
Hydrogen Permeation and Mechanical Behavior of Hydrogen-Charged CR-Coated and Oxidized ZR-Alloy Fuel Cladding
No full textFollowing the Fukushima nuclear accident in 2011, accident tolerant fuel (ATF) became a major research interest to enhance the safety of light water reactors. Because the current fleet of light water reactors and their associated fuel cycle infrastructure are well-established, developing ATF that does not significantly alter the current fuel form or change its supporting infrastructure is ideal. Cr-coated Zircaloy fuel cladding is therefore an excellent short-term solution to enhance accident tolerance. Thin layers of Cr-coating offer notable resistance to corrosion, high-temperature oxidation, physical wear, and hydrogen permeation. Cold spray (CS) and physical vapor deposition (PVD) are two well-established Cr-coating methods for nuclear fuel claddings. However, the quality and microstructure of the Cr coatings deposited with these methods can vary significantly, possibly impacting the in-reactor performance of these coatings.
It is also well known that excessive hydrogen uptake leads to the formation of Zirconium hydrides, which can cause cladding embrittlement. Hydride embrittlement increases risks of cladding fracture and ultimately sets the operational limits of the fuel. Therefore, fully understanding the hydrogen permeability and embrittlement phenomena associated with the new Cr-coated claddings is of great importance. This study specifically aimed to test the hydrogen permeability of Cr-coated fuel claddings produced through CS and PVD coating methods. To better represent in-reactor conditions, some claddings were also oxidized in a pure water autoclave environment before testing. Claddings were subjected to gaseous hydrogen charging and subsequent hydrogen content analyses to quantify the efficacy of Cr coating (and its oxidized form) as a hydrogen permeation barrier. Because fuel claddings experience the greatest stress in the circumferential direction, ring tension testing (RTT) was conducted on the hydrogen-charged claddings to evaluate the effects of coatings and hydrogen uptake on their mechanical strength and ductility. The Cr coatings and oxide layers proved to be highly effective in reducing hydrogen uptake. The Cr- and Zr-oxides had the lowest hydrogen permeability, followed by pulsed PVD and nitrogen-propelled CS Cr coatings, respectively. The studied hydrogen contents (10~190 wppm) had no significant effect on mechanical strength or overall ductility, with variations less than 2% and 10%, respectively. Thicker Cr coatings, specifically cold sprayed coatings, only slightly reduced overall ductility but changed necking behavior