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The current study investigated the roles of executive functions and emotion processing in college adjustment among students with ADHD symptoms enrolled in the Support for Students with ADHD or ADHD-like Experiences (SHIELD) Program at the University of North Carolina at Charlotte (UNCC), an intervention program designed to support students with executive dysfunction Using the Perception-Valuation-Action cycle (PVA) as a theoretical framework, the study examined whether changes in executive functions (inhibition, working memory, shifting) and emotion processing (emotional awareness, emotional clarity, cognitive reappraisal) across a semester were associated with academic, social, and psychological adjustment, as well as with ADHD symptoms. At baseline, students endorsed clinically significant ADHD symptoms and executive dysfunction, with lower psychological adjustment compared to non-ADHD college samples. While no statistically significant changes occurred from pre- to post-semester, students showed small improvements across all adjustment domains. Unexpectedly, decreased inhibitory control was associated with better academic and psychological adjustment, challenging traditional deficit-focused models of ADHD. Increases in emotional awareness and cognitive reappraisal were significantly associated with better social adjustment, while emotion processing abilities showed stronger associations with hyperactive and impulsive symptoms than executive functions. These findings provide preliminary evidence for the SHIELD program's efficacy while suggesting that reduced inhibition may serve adaptive functions in college environments and highlighting the importance of emotion processing abilities for adjustment.
Teacher stress and teacher burnout are not novel terminology for the education field. The purpose of this study is to describe beginning teachers’ perceptions of job-related stress, school-level resources available to help mitigate this stress, and how they cope with their stress. The framework that supports this qualitative, phenomenological study is the transactional theory of stress and coping (Lazarus & Folkman, 1984). Guided by the following research questions 1) In what ways do beginning teachers perceive job-related stress? 2) In what ways do beginning teachers perceive contributors to stress in the workplace? 3) In what ways do beginning teachers perceive to be preventative to help alleviate these stressors? Key findings included descriptions of teacher stress, contributors to job-related stress, and coping mechanisms. This study implores school districts and academic researchers to act to improve the experiences of beginning teachers by providing more active and accessible support, realistic workload expectations, and attainable coping mechanisms.
In the Piedmont and Blue Ridge of North Carolina, crystalline rock aquifers provide drinking water to over 2.4 million residents, where geogenic arsenic, chromium, vanadium, and manganese exceed health advisory levels for safe drinking water. These trace elements tend to co-occur with groundwater, mostly redox-sensitive elements with similar chemical and thermodynamic properties, such as affinity for adsorption and nearby pe-pH boundaries in aqueous environments. While a single contaminant in drinking water can be toxic to public health, having multiple contaminants in the same water increases the health risks multiple times. This dissertation characterizes the geochemical and hydrological controls on the release of trace metals from parent rock to moving groundwater, including the co-occurrence of (i) arsenic (As) and manganese (Mn), and (ii) chromium (Cr) and vanadium (V).
In Research Study 1, two proposed redox frameworks (V1 and V2) were applied to the NCWELL water quality data of approximately 53,000 private wells without DO but containing partial redox parameters of 8 crystalline rock terranes in the Piedmont and Blue Ridge of NC. Redox parameters pH, NO3-, Mn, and Fe were used to classify water samples. Nitrate was used as the reliable oxic state indicator, Mn as the reliable anoxic state indicator, and pH to categorize water samples into four groups—probable oxic, anoxic, oxic/anoxic, and no pH categories—before applying the V2 framework. In both frameworks, approximately 89% of the samples were successfully classified into I-oxic, II-suboxic/low NO3-, III-mixed, IVA-Mn reducing, and IVB-Fe reducing categories. Across all terranes, 55 to 61% of the water samples were either oxic or low-nitrate oxic, whereas 21% were anoxic, and 18 to 24% were in the mixed redox category. However, significant differences were found between individual terranes. Charlotte Terrane was characterized by the prevalence of oxic conditions ( 69% in V1; 65% in V2), while Carolina Terrane had the most anoxic conditions (30% in V1 and V2). These differences could be attributed to geological variations, with aquifer mineralogy serving as the primary factor and redox conditions acting as the secondary factor. The redox classification frameworks are valuable for categorizing water samples lacking DO and addressing missing data, a common issue in large water quality datasets. Additionally, the insights gained from the redox distribution and framework development in this study can be applied to other datasets for a deeper understanding of redox conditions.
Research Study 2 examines the geochemical evolution of groundwater along representative flow paths in the Charlotte and Carolina Terranes to quantify the dominant reactions influencing pH and redox conditions, which are the master variables of trace element behavior from initial to final water masses. Field-collected water chemistry data were integrated with expected mineral assemblages, inverse models generated by PHREEQC, and calculated pe values. A representative flow path in the Carolina Terrane is more geochemically evolved overall (–2.45E-04 mmol/L), with higher net redox mole transfers compared to a representative flow path in the Charlotte Terrane (- 4.53E-04 mmol/L). Reducing conditions are more dominant in the Carolina Terrane, and oxidizing conditions in Charlotte, consistent with the broad regional trends observed in Research Study 2.
Research Study 3 examines redox-sensitive elements (As, Cr, V, and Mn) and their co-occurrence at two research sites in the Charlotte (LGMR) and Carolina (NCZGMR) Terranes, advancing DEQ/USGS studies by introducing (i) trace element speciation and (ii) discrete depth sampling. In both sites, the proportion of water samples with As (> 0.1 µg/L) and Mn (> 50 µg/L) increases with pH, alkalinity, but decreasing DO, with As remaining as As(III) and Mn as Mn(II) in the near-neutral to slightly alkaline pH range (6.5 to 8). Statistical analysis of the Carolina Terrane samples showed a strong co-occurrence (ρ = 0.8) between As and Mn, with elevated As levels frequently exceeding the EPA MCL. Despite high Cr (median, 0.3 µg/L) and V (median, 1.3 µg/L) concentrations in the mafic aquifers of the Charlotte Terrane, no consistent co-occurrence of these elements is observed. Most Cr occurred as Cr (VI) (median, 0.3 µg/L), exceeding the NC Health Advisory Limit of 0.07 µg/L, and V as V(V). Overall, Cr and V do not co-occur in the Piedmont groundwater, with both elements largely existing in their most soluble and toxic forms (Cr(VI) and V(V)).
These contributions enhance the understanding of groundwater chemistry, geochemical evolution, and redox processes of redox-sensitive elements, while also providing a valuable tool for detecting trace metals that may exceed recommended levels, thereby helping to prevent public health risks. These hydrochemical drives can be utilized to identify areas of higher risk and predict the occurrence and co-occurrence of trace metals in the Piedmont Groundwater System. On a broader scale, this knowledge can support the management and protection of sustainable water supplies, ensuring their long-term viability for current and future residents.
This dissertation investigates contemporary issues in corporate governance and financial reporting through three distinct but interrelated essays. The first study examines the economic impact and stock market consequences of a potential delisting threat faced by U.S.-listed Chinese companies under the Holding Foreign Companies Accountable Act (HFCAA). Using a large sample of cross-listed firms from China and other Asian economies, the analysis documents significant underperformance of Chinese firms during the HFCAA Legislative and Effective Periods, translating into substantial wealth losses for U.S. shareholders. Although a bilateral agreement later mitigated the delisting risk, these findings highlight the importance of transparent auditing standards and consistent regulatory enforcement in cross-border listings. The second essay centers on short sellers’ informational advantages in predicting firms’ financial misstatements. Drawing on a comprehensive set of restatement announcements, the study challenges prior research that portrays a pronounced inverted U-shaped pattern of short interest around misstatements. Instead, the evidence suggests that heightened short-selling activity is closely tied to short sellers’ ability to process adverse public information—such as analyst downgrades, poor earnings releases, and legal or regulatory troubles—rather than their exclusive access to non-public insights. The third essay focuses on discussions of racial diversity and Diversity, Equity, and Inclusion (DEI) commitments within corporate settings. By investigating firms’ public communications and subsequent policy outcomes, this paper sheds light on how board-level or organizational discourse surrounding racial diversity correlates with tangible DEI initiatives and broader stakeholder responses. The results emphasize how transparent communication and inclusive leadership practices can shape organizational reputation and performance. Collectively, these three essays underscore the essential role of governance, regulatory oversight, and transparent disclosure in safeguarding investors and preserving market integrity. By examining cross-listing regulations, short-selling behavior around misstatements, and corporate diversity commitments, the dissertation offers new insights for policymakers, practitioners, and scholars seeking to foster responsible financial reporting and equitable business environments.
The present dissertation research aimed to identify factors that influence food purchase for families with young children, and further, to understand if family communication patterns play a role in the ways families communicate about food.
Three studies were conducted to address these aims. An online questionnaire focused on food purchase influences, food purchase discussion, and family communications patterns was administered to the primary food shopper for families with elementary school-aged children living in the United States. The first study utilized data from this online questionnaire to determine the factors most frequently influencing food purchase and to determine the association between income and the influence of certain factors on food purchase. The second study also used data from the questionnaire and identified the frequency of food purchase discussion within the sample, as well as the association between income and high levels of conversation. STATA 16 data analysis software was utilized to perform logistic regression and calculate unadjusted and adjusted odds ratios and 95% confidence intervals for each of the first two studies. The third study utilized qualitative interviews of questionnaire participants from each of the four family communication pattern types to collect data and thematic analysis was used to determine how families with children communicate about food or other purchases.
Participants in the online questionnaire reported availability, buyer preference, household preferences, and convenience as most frequently influencing their food purchases of both fruits/vegetables and “junk” food, or food generally considered non-health promoting. For fruits and vegetable purchases, there was a statistically significantly increased odds of marketing as an influence in households with income of less than $50,000 compared with households with an income of $100,000 or more (OR=9.01; 95% CI: 4.06, 20.01), and the association was attenuated, but remained statistically significant when adjusting for numerous demographic characteristics (AOR=7.56; 95% CI: 2.15, 26.52). Additionally, households in the lowest income group had statistically significantly increased odds of food quantity influencing fruit/vegetable purchases when compared to the highest income group, although findings were no longer statistically significant after adjustment for marital status, education, and age. (OR=2.66; 95% CI: 1.05, 6.74 and AOR=1.27; 95% CI: 0.26, 6.14). There were similar associations for “junk” food, in that the lowest income group had increased odds of marketing (OR=4.21; 95% CI: 1.91, 9.30 and AOR=2.21; 95% CI: 0.77, 6.36) and food quantity (OR=2.53; 95% CI: 1.16, 5.52 and AOR=4.05; 95% CI: 1.08, 15.23) influencing their purchasing when compared to the highest income group, although adjusted results were only statistically significant for the food quantity outcome. For “junk food”, the lowest income group also had increased odds of food beliefs influencing their purchase compared to those with incomes $100,000 or more (OR=3.02; 95% CI:1.39, 6.60 and AOR=2.42; 95% CI: 0.79, 7.43).
The second study, which utilized data collected from the online questionnaire, revealed that nearly 94% of participants reported discussing food purchase with their families, and over half (51%) of participants reported high levels of conversation orientation. Households with incomes $50,000-$99,999 had increased odds of reporting high levels of conversation compared to those with incomes of $100,000 or more (OR=2.43; 95% CI: 1.06, 5.57), and these odds remained similar in an adjusted model including both income and education (AOR=2.61; 95% CI: 1.23, 5.60).
The qualitative study reaffirmed that many families talk about food purchase and want children to understand the cost of items that are purchased. The family communication pattern types derived from the scale used in the questionnaire were often not evident based on responses given during the interviews. Reliance on the primary shopper to do all parts of the food acquisition process was a prominent theme derived from the interviews, and while some preferred this, others noted wishing they had more assistance from their partner or other family members. Participants also frequently noted the cost of food as a major concern more generally, and that price does play a major role in what they decide to purchase when shopping.
The findings from these three studies indicate that families are communicating about food purchase and identifies key purchasing influences. Regardless of a family’s communication about other topics, food purchase discussion may be an opportunity for children to influence the food that is brought home by the primary food shopper. Thus, health professionals should consider leveraging discussions of balanced diets with both children and caregivers to influence the purchase of healthy food options for consumption.
As the adoption of electric vehicles (EVs) grows, optimizing the EV charging process becomes crucial for efficient energy utilization and grid management. Some key aspects related to EV charging optimization include charging cost minimization, battery health and efficiency optimization, EV user behavior analysis, EV charging stations placement or location optimization etc. Through this dissertation, our aim is to contribute to the field of EVs in a way that helps in optimizing the huge infrastructure that will be required in coming years to accommodate the large number of EVs on roads.
In the first chapter, the problem under consideration is optimizing the charging schedule of an electric vehicle to minimize the total charging cost. In order to preserve battery life, the charging rate is restricted based on the current state-of-charge (SoC) of the battery. Noting that the charging rate limit is typically represented as a decreasing concave function of SoC, we first formulate the problem as a constrained optimal control problem. Discretization of the problem poses a nonlinear programming (NLP) problem that has a linear objective function and a convex feasible region. The proofs of the convexity of the feasible region and the strong duality of the problem are presented. Exploiting the strong duality, we present an exact solution approach that employs a cutting plane method to solve the Lagrangian dual problem in conjunction with the recovery of the primal solution. Subsequently, we propose two heuristics that employ greedy strategies, where charging is conducted to its rate limits over the periods with the lowest costs. We also present examples that illustrate these greedy strategies may not yield exact solutions. A thorough numerical experiment on simulated data is provided for the comparative efficacy of the proposed methods to the existing method.
In the second chapter, we consider a disjointly constrained bilinear program in which the variables are partitioned into two disjoint sets, each with its own respective set of constraints while bilinear terms in the objective function relate the two sets of variables. Leveraging the fact that the problem reduces to a linear program when one set of variables is held constant, we initially provide a geometric characterization of the relationship between the optimality condition of a basic feasible solution within one set of variables and the corresponding polyhedron in the other set of variables that achieves optimality, which we call the optimality polyhedron. By exploiting this relationship, we propose an algorithm that implicitly enumerates basic feasible solutions to expand the set of optimality polyhedra that eventually covers the feasible region in the other set of variables. A numerical study on randomly generated instances reveals that the proposed algorithm examines only 3.56% of the total number of basic feasible solutions on average while generating higher quality solutions compared to an off-the-shelf solver.
In the third chapter, we formulate the hard clustering problem of assigning a data point to exactly one cluster as a bilinear optimization problem. Using the Manhattan distance between points and their respective cluster centers as the objective function, the optimization problem is formulated as a minimization problem. This bilinear optimization problem is solved using the polyhedra expansion algorithm with initial clustering from k-means algorithm. The application of this methodology is demonstrated on the National Household Travel Survey data by utilizing the basic demographic and psychographic variables. The objective of this study is to develop a clustering scheme that can be utilized as a baseline to create potential customer segments by analyzing the driving behavior of a group of EVs.
β-ZnTe(en)0.5, an organic-inorganic hybrid material, demonstrates exceptional long-term stability exceeding 15 years in ambient conditions, surpassing materials like perovskites. This stability arises from strong covalent-like bonds within its quasi-2D layered structure, where ZnTe inorganic layers are bonded with ethylenediamine. The material exhibits quantum confinement effects, resulting in a significant bandgap blueshift compared to bulk ZnTe, and anisotropic thermal expansion with a low uniaxial thermal expansion coefficient. High crystallinity, evidenced by narrow Raman line widths, further highlights its quality, making β-ZnTe(en)0.5 a promising candidate for optoelectronic applications.
β-ZnTe(en)0.5 crystals were synthesized via a solvothermal method, leveraging high autogenous pressures and controlled crystallization in sealed reaction vessels at elevated temperatures. This technique, using ethylenediamine as a solvent, facilitated the formation of colorless flake-like crystals through controlled reaction parameters and purification. For device fabrication, a shadow mask approach was chosen over conventional lithography like UV photolithography and electron beam lithography (EBL), offering a resist-free, versatile, and less invasive patterning method that avoids chemical exposure and preserves material integrity. This allowed for high-quality devices with minimal artifacts, enabling reliable transport property investigation.
To explore charge transport, Space-Charge-Limited Current (SCLC) analysis, based on the Mott-Gurney law (J ∝ V²), was employed. While real materials deviate from ideal behavior, this analysis established a baseline understanding of charge carrier mobility. Two-probe electrical measurements on both vertical and lateral device configurations were conducted. Initial vertical measurements on pristine samples with metal electrodes revealed SCLC behavior, with mobilities of 8.8 × 10-3 cm2/(Vs) and 2.5 10-3 cm2/(Vs) for samples synthesized in 2007 and 2019, respectively. Lateral measurements, conducted along the a- and c-axes, revealed significantly higher mobilities, with values of 1.787 × 102 cm2/(Vs) along the a-axis and 1.696 × 101 cm2/(Vs) along the c-axis, demonstrating anisotropic charge transport. These results underscore the importance of SCLC measurements in characterizing the anisotropic transport properties of materials.
Complementary electronic structure analysis using X-ray photoelectron spectroscopy (XPS), ultraviolet photoelectron spectroscopy (UPS), Kelvin probe force microscopy (KPFM), and hot probe measurements were performed. XPS revealed a valence band maximum (Ev) of 0.80 eV below the Fermi level (EF), indicating p-type conductivity, corroborated by a 3.55 eV bandgap from photoluminescence (PL). KPFM yielded a work function of 4.59 ± 0.03 eV, consistent with UPS data. Integrating these results produced a reliable energy band diagram, confirming p-type conductivity and establishing band alignment. This multi-technique approach emphasizes its importance for accurate electronic structure determination.
In this dissertation, we discuss invisibility and inverse problems.
First, we discuss nonradiating orbital motions. We theoretically create nonradiating sources that orbit about the center of an annulus and are of whatever shape we want them to be. And we also discuss how one could possibly create an experimental setup demonstrating nonradiating orbital motions. The examples we discuss are all 2-D scalar wave problems.
Then, we discuss nonscattering scatterers. Building on the work of A. J. Devaney and others, we discuss about how to theoretically create objects that are invisible from some directions but not others. We find that, the more directions of invisibility our objects have, the harder they become to see when looking at them from between the object's directions of invisibility.
Finally, we discuss a globally convergent numerical method for solving a coefficient inverse problem. In particular, we discuss a Carleman-Picard iteration method for reconstructing the coefficient function for an inverse problem involving a parabolic PDE. The coefficient function can represent, among other things, a hidden object that we want to find without disturbing the medium in which our object is hidden. We demonstrate how well our new numerical method works with three tests.
As the automotive industry transitions toward higher automation, Level 2 automated vehicles (AVs) represent a pivotal phase where human oversight remains essential. This study examines car-following (CF) behavior, a key factor in traffic efficiency and safety, within mixed traffic environments that include both Level 2 automated vehicles (AVs) and human-driven vehicles (HDVs). The objectives are to (1) analyze steady-state CF behavior between AVs and various HDV types, (2) to calibrate CF parameters to understand the behavior of level 2 AVs with different types of HDVs, (3) to compare the CF behavior of level 2 AVs with different aggressiveness types and different vehicle types of HDVs, and (4) to compare the CF behavior of level 2 AVs with different aggressiveness types across diverse facility types.
A mixed-methods approach using real-world trajectory data and simulations was employed to model and calibrate CF behavior using the Intelligent Driver Model (IDM) and the Gipps model. Key findings show that aggressive AV settings can increase traffic capacity but have higher safety risks, while mild settings promote safer interactions through increased spacing. HDVs adjust their behavior based on AV characteristics, maintaining a greater distance behind more aggressive AVs. Roadway type also affects CF dynamics—urban and signalized roads lead to more cautious driving behavior than freeways. These insights refine CF models, enhance AV simulation accuracy, and support safer integration of AVs into real-world traffic systems.
We propose frameworks for dimension reduction in high-dimensional Vector Autoregressive (VAR) models using Spatial Quantile Regression (SQR). By incorporating adaptive Lasso and SCAD regularization, our methods enable robust inference under heavy-tailed or non-Gaussian errors while performing automatic
variable selection. To further address over-parameterization, we develop a tensor-based approach—Multilinear Low-Rank Spatial Quantile Regression (MLRSQR)—which restructures VAR transition matrices into low-rank tensors for simultaneous parameter reduction and quantile-wise modeling. Additionally, we
introduce the Sparse Higher-Order Reduced-Rank SQR (SHORRSQR) estimator, integrating Lasso penalties for sparsity, and design efficient ADMM-based algorithms.
