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Recurrent events are commonly encountered in medical and epidemiological studies. It is often of interest what and how risk factors influence the occurrence of events. While much research on recurrent events has addressed both time-independent and time-dependent effects, there is a possibility that these effects also vary with certain covariates.
In this dissertation, we develop novel estimation and inference procedures for two intensity models for recurrent event data—a class of semiparametric models and a nonparametric frailty model. Both models allow for the simultaneous measurement of time-varying and covariate-varying effects, with covariates potentially depend on event history. The proposed semiparametric models offer much flexibility through the choice of different link functions and parametric functions. Two hypothesis tests have been developed to assess the parametric functions of the covariate-varying effects. For the proposed nonparametric intensity model with gamma frailty, estimation procedure involves using an Expectation-Maximization (EM) algorithm and local linear estimation techniques. Variance estimators are obtained through a weighted bootstrap procedure. Both of the proposed models have been applied to a malaria vaccine efficacy trial (MAL-094) to assess the efficacy of the RTS,S/AS01 vaccine.
The COVID-19 pandemic abruptly changed educational institutions globally and challenged teachers and students. This immediate shift was difficult for K12 teachers because they were required to teach their courses online or using a blended learning (BL) model. As BL use continues to grow, concerns about student-content engagement have emerged. This study used a single case study to investigate experiences with facilitating student-content engagement in BL environments. Eleven teachers from two districts were surveyed and individually interviewed with semi-structured interviews. The complex adaptive blended learning systems (CABLS) was used as the theoretical framework to guide the data collection, data analysis, and interpretation of results. Findings of this study revealed that the learner demographics showed a diversity in economic status and academic abilities, with socioeconomic status emerging as a potential indicator affecting students’ access to technology within BL environments. Digital literacy skills varied among students, influencing their student-content engagement in BL environments. Teacher experiences with BL varied from embracing the mix of technology-mediated instruction with traditional F2F methods. Additionally, results showed that the support systems such as instructional coaches and professional learning communities (PLCs) played a crucial role in facilitating student-content engagement and enhancing pedagogical practices. Furthermore, findings showed that districts and institutions have demonstrated commitment to supporting BL environments through multiple layers of support. As technology continues to evolve, addressing challenges and leveraging collaborative efforts will be essential in ensuring that BL environments thrive and promote meaningful student-content engagement. Implications of this study inform the development of best practices, guidelines, and resources to enhance student engagement and foster a culture of continuous improvement in BL environments to improve student learning outcomes.
UNDERSTANDING CONSUMERS’ INTENTION TO ACT ON SOCIAL MEDIA INFLUENCERS’ COSMETIC SURGERY RECOMMENDATIONS
(Under the direction of Dr. Jared Hansen)
A growing concern is how social media is redefining how consumers view themselves and their choices to reshape their physical bodies. There is a stream of research that indicates that attractiveness is important to people. Some studies focus on the perceived benefits of attractiveness in their authenticity. A different stream has started to look at coolness. Other studies have focused on attractiveness and envy. This research combines all of these different reasons together, comparing how they work in tandem, with a new lens of focus: consumers’ views of the attractiveness, authenticity, and coolness of the social media influencer, and how those elements in tandem, in combination with envy, impact consumers' behavioral intention to do the things (e.g., cosmetic procedures or surgeries) recommended by the influencers. Additionally, it examines if potential envy antecedents of (a) attractiveness to improve job opportunities versus (b) attractiveness to ‘fit in' vary depending on the consumer life stage. I elaborate on implications for future research related to marketing and society, marketing managerial practice, and consumer well-being.
Keywords: Instagram; social media influencer; technology acceptance model (TAM); structural equation modeling; attractiveness; authenticity; coolness; envy; fitting in; career opportunities; cosmetic surgery
It is no secret that technological innovations have interrupted businesses in every sector, across all industries and the Accounting profession is no exception. Some of the most disruptive technologies include Artificial Intelligence, Big Data Analytics, Machine Learning, Blockchain and Robotic Process Automation and affect entry-level positions. The Big Four Accounting firms are now responding by investing in these technologies to stay ahead of the competition. Due to these significant investments, entry-level accountants need to acquire new technical skills to become employable. Using signaling theory as a springboard, my study seeks to examine whether job seekers with technology skills will apply to the accounting profession based on the investment signals from these firm. The study also examines the influences of perceived ease of use, perceived usefulness, gender and race.
In recent years, the global energy sector has been undergoing a significant transformation, characterized by an increasing shift towards data-driven operations and the widespread adoption of renewable energy such as solar photovoltaics (PV). This transition is largely motivated by the urgent need to address climate change and the realization of the potential that large-scale data collection and analysis hold for enhancing energy efficiency and sustainability. As the energy landscape becomes more complex and interconnected, the role of sophisticated energy forecasting techniques has grown in importance. These techniques are crucial for managing the variability and uncertainty inherent in renewable energy sources, such as wind and solar power, which are subject to fluctuations in weather and environmental conditions. Moreover, the integration of big data analytics into energy systems facilitates more accurate and timely predictions, thereby enabling more effective planning, operation, and maintenance of energy infrastructure. This dissertation introduces a novel, data-driven methodologies to address key challenges in energy forecasting: predicting weather-induced power outages, net load forecasting, and accurately estimating solar PV penetration.
In the first part of the study, a methodology to forecast weather-related power distribution outages one day ahead on an hourly basis is presented. A solution to address the data imbalance issue is proposed, where only a small portion of the data represents the hours impacted by outages, in the form of a weighted logistic regression model. Data imbalance is a key modeling challenge for small and rural electric utilities. The weights for outage and non-outage hours are determined by the reciprocals of their corresponding number of hours. To demonstrate the effectiveness of the proposed model, two case studies using data from a small electric utility company in the United States are presented. One case study analyses the weather-related outages aggregated up to the city level. The other case study is based on the distribution substation level, which has rarely been tackled in the outage prediction literature. Compared with two variants of ordinary logistic regression with equal weights, the proposed model shows superior performance in terms of geometric mean.
The dissertation then explores net load forecasting in the context of increasing behind-the-meter (BTM) solar PV system adoption. This adoption introduces complexities to grid management, especially concerning net load-the difference between demand and PV generation. The intermittent nature of PV generation, influenced by weather and time, adds to net load volatility, posing challenges to grid reliability. This dissertation presents a review of state-of-the-art net load forecasting with a focus on forecasting approaches, techniques, explanatory variables, and the impact of PV penetration on net load forecasting. Additionally, the study conducts a critical analysis of existing literature to identify gaps in the field of net load forecasting and PV integration. To address some of these gaps, a benchmark net load forecasting model is proposed. The proposed model uses publicly available data from ISO New England. Through the case study, it is demonstrated that the proposed net load forecasting model outperforms the current benchmark load forecast model significantly in terms of forecasting accuracy, as measured by Mean Absolute Percentage Error. Moreover, the case study also demonstrates the effectiveness of the proposed model over a range of PV penetration, which is an important consideration as the use of solar energy continues to grow.
Furthermore, the dissertation addresses two critical questions regarding PV integration: (1) How much PV is there in the system?; (2) Which meters have BTM PV? To address the challenge of estimating PV penetration in systems, existing supervised and unsupervised methods are reviewed, which reveal common limitations, especially when PV installation information is limited or completely unavailable. To overcome these challenges, a regression-based approach is developed by leveraging the difference in performance in the benchmark load and net load forecasting models in forecasting net load. The proposed framework is deployed for real-world data from an ISO and a medium-sized in the United States. The results validate the effectiveness of the proposed method in accurately estimating PV penetration levels, even without explicit PV installation data, using only historical load data.
The final part of the study focuses on identifying meters with BTM PV installations. Again by, leveraging the performance disparities between load forecasting models and net load forecasting models, a methodology is devised to differentiate meters with and without PV installations. The effectiveness of the proposed frameworks is confirmed using an empirical case study at a medium-sized US utility with meter-level load data meters. The results illustrate that accurate identification of meters with PV installations was achieved while maintaining a low rate of false identifications. This methodology provides valuable insights for utilities, empowering them to comprehend the adoption and impact of distributed solar energy within their service territories.
Overall, this study contributes significantly to the field of energy system forecasting by developing data-driven models that enhance the understanding and management of weather-induced outages, net load variability, and solar PV integration. These advancements enable utilities to make informed decisions for grid planning, capacity management, and service customization, paving the way for more resilient and efficient energy systems.
This dissertation evaluates the fatigue response of a steam header designed to mirror the specifications of an ex-service unit, with a focus on optimizing material selection through a detailed analysis involving cost, performance, and durability. Beginning with a study comparing three different alloy choices, 2.25Cr-1Mo, 9Cr-1Mo-V, and IN740H, headers are developed and compared using the procedures outlined in ASME BPVC. The design of the headers follows that used in the original development, and their performance is evaluated in representative loading transients. Each of the designs is evaluated for their fatigue response using the finite element program Abaqus. The results demonstrate that cost savings would likely outweigh any performance benefit to the current system.
The second portion evaluates the material characteristics of 2.25Cr-1Mo following years of exposure to a harsh operating environment. Material specimens were machined from the ex-service unit and subjected to uniaxial testing at various temperatures. The process is used to establish the Chaboche NLKH hardening coefficients. The selection of the NLKH model was guided by its capability to capture the cyclic behavior of the material. The material results are used to compare the projected performance of the 2.25Cr-1Mo header found using readily available material acquired from virgin specimens and those found from the existing unit. The results demonstrate a markedly reduced strength in the service-exposed material, illustrating the effects of the material transformation that occurs over time. This study highlights the importance of operational wear on the projected performance of the header.
The final portion introduces an automated crack growth algorithm in combination with Abaqus to model the progression of a seam crack within a 2.25Cr-1Mo header. Traditional fatigue assessments consider the formation of surface cracks as the end of usability. However, it is well established that the existence of cracks in headers may be allowable, depending on several factors such as size, location, and material. Additional challenges exist in headers along the tube-header intersections, which suffer from non-uniform crack propagation stemming from the complex thermal-mechanical loading near the intersection. To address this issue, the present work develops an algorithm in Abaqus to use the seam crack capability and Paris law to efficiently perform iterative crack growth simulations. This approach captures the uneven growth response of the crack, providing more realistic service life estimations.
The quality of life for those who support loved ones living with substance use disorder (SUD) is adversely affected due to destructive behaviors and the impact these behaviors have on the family system (Kaur, 2016). Consequently, primary support persons (PSP) often live their lives in silence and experience disenfranchised losses that impact not just the family unit but also impacts the human system, the most significant system among family units (Howard et al., 2010). This same researcher asserts this circular causality is almost always found among human and family systems as the actions of one person create responses or adaptions from other persons living within that same family unit. This is important because it highlights the way alcohol and other drugs (AOD) impact normal functioning of the addict, their loved ones, and society (Cudak & Pedagogika, 2015).
The purpose of this study was to examine variables that impact of quality of life of caregivers to people living with SUD. Perceived losses due to a loved one’s SUD, perceived social support, one’s own substance (ab)use, and stress were all examined to learn the impact these variables have on QOL. Multiple linear regression was utilized to examine the impact on QOL (n = 114) as predicted by losses, perceived support, substance use, and stress. Results indicated that support, losses, and stress are significantly associated with the dependent variable QOL (r2 = .815) to QOL. Results of this study postulate insight into future treatment approaches with PSP and highlight links to treatment that need to be addressed on behalf of PSP as well as the total family unit. These findings have implications for mental health and substance abuse counselors in terms of working with PSP and examining how improved QOL of support persons impacts those being treated for SUD. Future research is needed to examine how more thorough and more inclusive treatment approaches can include working with families of those who are addicted to substances.
Keywords: Quality of life, primary support person, substance use disorder, families, addiction, losses, depression and stress, support, family support, SUD treatment, family treatment involvement, support person
Random antireflective surface nanostructures (rARSS) enhance transmission by reducing the electromagnetic impedance between optical indices across a boundary, serving as alternatives for traditional coating techniques. Understanding and quantifying the role of randomness of the surface nanostructures remain elusive, without a comprehensive model that can accurately predict the wideband spectral response of randomly nanostructured surfaces based on causal physical principles. Effective-medium approximations (EMA) emulate the randomly structured surface as a sequence of homogeneous film layers, failing to predict the critical (or cut-off) wavelength above which the enhancement effect is observed and below which bidirectional optical scatter is prominent. Analyzing near-field or far-field radiance due to wavefront propagation through randomly nanostructured surfaces requires high computational budgets, which are challenging for randomly distributed features with varying-scale boundary conditions.
Deterministic periodicity is considered a sufficient surface geometrical descriptor for regular (or long-range repetitive) nanostructured surfaces, whereas characterizing random surface features is based on first-order statistical evaluations or macroscopic averages, such as autocorrelation lengths, which introduce significant ambiguity in subwavelength scales. What constitutes the "randomness" of rARSS, beyond standard surface topography measures, is subjective. Conventional optical surface structure characterization, disregards aspects of nanoscale morphological attributes, mainly spatial configuration or organization, due to resolution limitations of metrological instruments. The organizational aspect of nanostructured features can significantly impact the macroscopic Fresnel reflectivity radiance, bidirectional scattering, and axial transmission enhancement (cooperative-interference effect).
In this work, transverse granule population distributions and their corresponding granular organization at the nanoscale, is determined using a variation of the Granulometric image processing technique. Various rARSS surfaces were fabricated, resulting in unique surface modifications and spectral performance, as observed with respectively scanning electron microscope (SEM) micrographs and spectral photometry. The approach to quantify randomness or complexity of the nanostructures, presented in this work, is based on Shannon’s entropy principles. Resolution limitations from conventional characterization techniques using non-invasive confocal microscopy and spectroscopic ellipsometry is discussed. Statistical quantification of nano-structural randomness using Shannon’s entropy is proposed as a solution to characterize the unique degree of disorder on the surfaces. A figure-of-merit is derived and computed from surface organization state variables, and it is proposed as a heuristic parameter to predict the transition from spectral scattering to the transmission enhancement region. This multivariate problem is addressed by accounting for the conditional probability dependence of granule populations as functions of granule dimensions and their corresponding proximity distributions, thereby laying the foundations for a surface microcanonical ensemble model, establishing a link between surface morphological descriptors and spectral variables.
This dissertation enhances the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) by integrating information privacy concerns, examining their influence on the adoption of web-based healthcare portals. Through a survey of 298 U.S. residents using healthcare technologies, the study investigates the interplay between UTAUT2 predictors—Performance Expectancy, Effort Expectancy, Facilitating Conditions, Habit, Social Influence, and Hedonic Motivation—and the intention to use these technologies, while assessing how privacy concerns modulate these relationships. Regression analysis highlights the positive impact of Performance Expectancy, Effort Expectancy, and Habit on adoption intent, with privacy concerns significantly moderating the relationship between Effort Expectancy and usage intention.
The research enriches the UTAUT2 model by showcasing the pivotal role of privacy concerns, thus advancing theoretical understanding and enhancing model predictability in the context of healthcare technology. Practically, it offers insights for practitioners and policymakers on addressing privacy concerns to improve technology adoption. This synthesis of privacy concerns within the technology acceptance framework paves the way for targeted strategies to increase the uptake of healthcare technologies, marking a significant contribution to both academic discourse and practical application in healthcare technology management.
Federal legislation for students with disabilities mandates that all students receive appropriate and relevant instruction across environments to improve postsecondary outcomes across domains. Teachers and parents alike have found that one way to meet individual student needs and increase instructional opportunities for students with disabilities is through the use of purposeful and meaningful community-based instruction (CBI). For students with extensive support needs (ESN), however, the practical implementation of CBI within the classroom and community setting may pose several barriers and relies heavily on teacher and family knowledge of community engagement strategies. Previous research in the area of CBI indicates that through the use of evidence-based practices CBI is effective in teaching skills across the four identified domains, which include leisure, vocational, community engagement, and daily living. In an attempt to bridge gaps in the available literature and research in the area of CBI, this study evaluated the effects of an intervention package comprised of three evidence-based practices (video modeling, visual supports, and system of least prompts), goal setting, and collaboration, through peer-implemented instruction, in order to teach leisure skills to young adults with ESN in relevant community settings. The experimental design was a multiple probe across skills replicated across two participants. Two young adults, ages 21 and 22 with ESN participated in the study, along with two of their same aged peers, and relevant team members/key stakeholders (i.e., program director at their university, parents). Three community-based leisure skills across three environments were chosen with a specific skill targeted at each location. The intervention was effective for teaching these leisure skills to the participants across all three community locations. In addition, they were able to generalize and maintain these skills at the conclusion of the study. Social validity measures indicated that all participants felt that these were relevant skills for the participants and that their role in this process was valuable. The findings from this study can be used to guide future research in the area of CBI with students of all ages to support them as they access community settings.
