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In this dissertation, we develop multivariate Dickman distribution and explore its properties. In addition, we utilize the Dickman distribution to model the small jumps within a broad class of Levy processes. Our central theorem establishes that the limit distribution of an appropriately transformed truncated Levy process with finite variation exhibits a Dickman-type Levy measure. We also provide equivalent conditions to further characterize this result. Drawing inspiration from this, we partition the Levy process into small and large jumps. Small jumps are effectively modeled by the Dickman distribution, while the remaining large jumps follow a compound Poisson distribution. Further, we extend our findings to Ornstein-Uhlenbeck (OU) processes. Our investigation encompasses two scenarios: the truncated OU process and the OU process driven by a truncated Levy process. In general, employing the same transformation outlined in our main theorem, we observe that the limit distribution of the truncated OU process aligns with a Dickman-type Levy measure. Notably, for the OU process with a truncated driving process, the limit distribution remains consistent with that of the OU process with a truncated driving process having a Dickman-type Levy measure.
Graph coloring is commonly used to schedule computations on parallel systems. Given a good estimation of the computational requirement for each task, one can refine the model by adding a weight to each vertex. Instead of coloring each vertex with a single color, the problem is to color each vertex with an interval of colors.
Stencil graphs appear naturally in the parallelization of applications, where the location of an object in a space affects the state of neighboring objects. Rectilinear decompositions of a space generate conflict graphs that are 9-pt stencils for 2D problems and 27-pt stencils for 3D problems. We show that the 5-pt stencil and 7-pt stencil relaxations of the problem can be solved in polynomial time. We prove that the decision problem on 27-pt stencil is NP-Complete. We evaluate the effectiveness of several different algorithms experimentally.
Executing graph algorithms in a parallel or distributed context is a challenging problem. It is possible that the algorithm picks a partial order with long chains, which limits its utility to parallel applications. We investigate how distributed dataflow graph algorithms obtain a partial order and how one could favor orders with shorter long chains. We study the behavior of these different algorithms on randomly generated RMAT graphs and real-world graphs. We show that our ordering methods can significantly reduce the length of the longest chain.
Approximately 25% of United States mental health practitioners are employed in private practice settings (BLS Data Viewer, 2021). However, the Council for Accreditation of Counseling and Related Educational Programs, which sets standards for graduate level counseling programs, neither specifies private practice settings as a specialization nor an area for graduate programs to emphasize. In addition, research specific to private practice counselors is limited to the challenges of working in the setting (Harrington, 2013; Legge, 2017). The purpose of this study was to explore the career development and current work experiences of new licensed counselors who earned the required direct client and supervision hours in the private practice setting. Basic qualitative research design, as described by Merriam and Tisdell (2016), was paired with Braun and Clarke’s (2021) Reflexive Thematic Analysis for this exploration. Due to the limited empirical research, Krumboltz’s (1979) Social Learning Theory of Career Decision Making was used as a theoretical framework. Supporting evidence from eight participants was analyzed and five patterns emerged: (1) Non-Counseling Experience is an Asset, (2) Graduate School Does a Great Job Giving you a Foundation to Build On, (3) Practical Experience Makes Up for Academic Gaps, (4) I’m Going to Start Private Practice and See What I Can Do, and (5) Private Practice Yields for My Career Development. These patterns and the accompanying ten subpatterns are discussed in alignment with extant literature and the theoretical framework. A private practice counselor development model was proposed amongst the other implications of the findings.
The purpose of this study was to explore the impact of the social construct of masculinity on the fraternity members’ experiences with accountability. This study focused on the methods fraternities used to hold their members accountable, how the members’ behavior changes, and the impact masculinity had on accountability methods. This study is rooted in an understanding of the concept of masculinity and how it is often engrained deeply in the fraternity experience (Harris & Edwards, 2010; Harris & Harper, 2015). This study used document review to understand espoused expectations and accountability process. Additionally, four participants each from two different fraternity chapters participated in semi-structured interviews. This qualitative phenomenological study sought to understand the experience each member had with accountability in their fraternity. After multiple rounds of coding, five themes were created encompassing the experiences fraternity members had with accountability: formal accountability only a formality, herd mentality: informal expectations of the group, informal accountability is the real accountability, being a man prioritized, and culture and context set the tone. The study largely found that masculinity had a strong influence over the accountability experience in the chapter. Additionally, informal accountability was used daily to enforce several informal expectations. Formal accountability and formal expectations, however, did not take priority in fraternities. The expanded understanding of accountability can be used by fraternity members and their advisors to help empower men to mitigate high-risk behavior. Doing so will be critical to ensuring fraternities remain a viable opportunity for undergraduate student involvement.
Values-based leader behavior is commonly referenced by scholars and practitioners as an effective style of leadership. Problematically, multiple definitions of the concept exist that are either ambiguous, tautological, or valanced. Additionally, the concept has been researched almost entirely via questionnaires with little triangulated evidence. The current study reviews previous conceptualizations of values-based leader behavior as well as the key components of leadership, values, and behavior to arrive at a new conceptualization framed from a signaling theory perspective: goal-oriented action or inaction signaling an individual’s, organization’s, or society’s value structure. Then, I review three commonly referenced manifestations of values-based leader behaviors (charismatic leader tactics, ethical leader signals, and transformational leader behaviors) and make the case that pay-for-performance strategies too are strategies that can signal one’s value structure. Using a pre-registered experimental design, I explore the extent to which each of these values-based leader behaviors influence stakeholder in- and extra-role behavior compared to a control condition in a realistic text labeling task. Results found that the pay-for-performance strategies were strong predictors of both in- and extra-role behavior, charismatic leader tactics were strong predictors of extra-role behavior, and the control condition produced the least net output for in- and extra-role behavior combined. I conclude with a discussion of the theoretical and practical implications as well as future research directions.
Materials science aims to explore the properties and behaviors of different materials, from metals to advanced carbon structures. This dissertation focuses on three distinct areas of study: Inconel Alloy 740H, polycrystalline graphene, and tetragraphene (TG).
The first part of this work concentrates on developing and validating a Chaboche unified constitutive model. This model incorporates both nonlinear isotropic and kinematic hardening rules to accurately predict the stress-strain behavior of Inconel Alloy 740H, a high-temperature nickel-based superalloy. The material parameters of the model are determined and its accuracy validated through experimental data obtained from uniaxial strain-controlled loading tests across a wide temperature and strain ranges.
The second part explores the mechanical properties of polycrystalline graphene, bridging scales from nanoscale to macroscale through a multiscale molecular dynamics (MD)–finite element (FE) modeling approach. By studying the behavior of graphene sheets with different grain boundaries and atomic structures, insights are gained into the influence of grain size on mechanical properties like the Young modulus and fracture stress.
The third part of this dissertation investigates the mechanical properties of tetragraphene (TG), a quasi-2D semiconductor carbon allotrope, with a focus on addressing graphene's limitations in electronic applications. Through MD simulations, the research examines TG's fracture properties under mixed mode I and II loading, considering variables such as loading phase angle, crack structure, and temperature.
The expectation for educators to engage in evidence-based decision-making has become standard protocol in public education, yet translating research into effective practice can often be mired with implementation challenges. Research-practice partnerships (RPP) support research-informed practice by engaging key stakeholders to address real and contextual problems encountered by K-12 educators. This qualitative critical realist case study investigated the inner workings and attributes of a teacher-centric RPP formed to address ongoing implementation challenges with the North Carolina Early Learning Inventory. Data sources included observations of RPP meetings, teacher interviews, communications, and analysis of artifacts. Thematic findings suggest that teacher-centric RPP models strengthen trust and credibility between educational agencies through a series of preconditions: Expanding access, diversifying perspectives, developing alliances, and deepening knowledge. This resulted in increased implementation practices and enhanced the production of usable information to address implementation fidelity. Simultaneously, this framework also heightened teachers’ sense of professional identity. This study contributes to a dearth of literature on applying RPP models to support evidence-based policy mandates and offers a new model for leveraging classroom practitioners. This investigation contributes to the field of evaluation by providing a sustainable model to maintain implementation fidelity and strengthen teachers’ perceptions of their professional identity and agency.
The purpose of this quality improvement project was to identify trends in the intraoperative dosing of dexamethasone in type II diabetic patients undergoing genitourinary procedures at a full-service community hospital and to determine impact of dosage level on postoperative glycemic response compared to preoperative blood glucose levels. Dexamethasone is a corticosteroid that has many dose-dependent benefits when administered perioperatively to surgical patients. It is often withheld in the type II diabetic population out of concern for effects on postoperative glycemic control due to the side effect of hyperglycemia.
The method of this quality improvement project consisted of a retrospective chart review of patients with type II diabetes undergoing genitourinary procedures. Data inclusion criteria included patients who: are type II diabetics, had procedures that lasted less than four hours, had a documented preoperative hemoglobin A1C reading within the last twelve months ranging from 6.5-8.9%, are non-pregnant, are not taking oral steroidal medications, and had an ASA classification of I, II, or III.
Forty-nine charts were reviewed and overall there was no significant change in blood glucose in the postoperative period (t = 0.92, p = 0.361). The dose of dexamethasone (4, 8, or 10 mg) had no effect on the change in blood glucose levels (t =-1.14, p = 0.263). Additionally, changes in blood glucose were not found to be associated with age, HbA1C, or ASA status.
Dexamethasone administration for patients undergoing genitourinary procedures had no significant impact on blood glucose levels in the postoperative period. These findings may be a result of the shorter length of surgery, in that all 49 charts in this sample consisted of different cystoscopy procedures and had an average surgery length of 66 minutes. Further study is needed to help facilitate anesthesia provider’s decision-making for dexamethasone dosing in type II diabetics.
Autonomous vehicles have gained huge interest across private industry, academia, government, and the public because they promise higher road efficiency, improved safety, better energy consumption, and improved emissions. However, the widespread adoption of autonomous vehicle technology will likely take place over several years (if not decades) as the technology becomes more widely accepted by the general public and more cost-effective. Therefore, there will be a long period of time when we have both autonomous and human-driven vehicles sharing the same road and it is essential to develop traffic management strategies that take the uncertainty associated with the heterogeneity in the traffic networks into account. Furthermore, it is crucial to understand the extent to which these control strategies improve the performance of the traffic network.
This research aims to develop, enhance, and validate hierarchical infrastructure-based control framework designs for improving the mobility of large-scale heterogeneous traffic networks. In this work, heterogeneity is defined as a multi-vehicle traffic network consisting of Human-Driven Vehicles (HDVs) and Autonomous Vehicles (AVs), distinguished by their operational characteristics and controllability. To capture the realistic nature of large-scale heterogeneous traffic networks, we adopt the heterogeneous (multi-class) METANET model wherein the density and velocity dynamics of each vehicle class in each cell are described mathematically.
Moreover, in this research, we propose a hierarchical distributed infrastructure-based control framework to manage large-scale heterogeneous traffic networks. At the lower-level, we employed the Distributed Filtered Feedback Linearization (D-FFL) controller which only requires limited information from the plant model. The purpose of this controller is to track the desired density of each vehicle class in the target cells which is set by the upper-level controller. D-FFL tracks the reference density by controlling the suggested velocity of vehicles in the target cell and its upstream cell. At the upper-level, in our initial design, a Distributed Extremum-Seeking (D-ES) controller is designed and implemented to find the optimal operating densities of each vehicle class in the target cells over time. Gradient-based D-ES is a model-free, real-time adaptive control algorithm that is useful for adapting control parameters to unknown system dynamics and unknown mappings from control parameters to an objective function. To improve the performance of the designed hierarchical controller and reduce the convergence time, we designed and implemented Lyapunov-based Switch Newton Extremum Seeking (LSNES) at the upper level of the hierarchy to feed the optimal density of each vehicle class in the target cells to the lower-level controller. One of the key distinctions between the Newton algorithm and the gradient algorithm is that the convergence of the former is not solely contingent on the second derivative (Hessian) of the cost map and it is user-assignable.
Finally, we established a MATLAB-VISSIM COM interface that allows closed-loop control of a simulated traffic scenario in PTV-VISSIM to test and validate the effectiveness of the distributed control approaches in large-scale traffic networks. The simulation results show that our control framework design can effectively reduce congestion and prevent congestion back-propagation during peak hours in large-scale traffic networks.
