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Considering the progressive growth of healthcare expenses in the national economy, many of those impacts are directly seen in the cost to our publicly and privately funded healthcare insurance programs. This research will utilize a version of the Behavioral Healthcare Utilization Model (BHUM) to conduct a comparative analysis of medical insurance payers and discharge dispositions. It will look at all payer types of healthcare funding in comparison to discharge disposition outcomes concerning acute care hospital admissions for patients that are age 45 to 75 and when diagnosed with Medical Severity Diagnosis Related Code 207, Respiratory Failure with Mechanical Ventilation Greater Than Ninety-Six Hours. This study will evaluate the lengths of stay, payer type, total charges of an acute care hospitalization as well as the discharge dispositions of those cases with the moderating effects of age, gender, and race on those relationships. The analysis will utilize the MS-DRG 207 as a control variable so that discharge disposition is moderated by the predisposing patient characteristics utilized by health systems and insurance payers to monitor per patient profit or loss variances. This research hopes to provide perspective on both the financial and quality of care aspects of a healthcare payer's influence on the healthcare system and how this impact may outweigh sociocultural and sociodemographic variables. Research objectives will benefit health system executives, healthcare insurance payer systems, and legislative planning committees. It will show how those differences in care management could affect long-term healthcare costs associated with care through hospital lengths of stay and payer influences on this utilization services.
The underrepresentation of Black women in Science, Technology, Engineering, and Mathematics (STEM) is a long-standing issue. According to the National Science Foundation (NSF, 2019), Black women hold less than 10% of STEM degrees, while only 2% work in STEM fields. These disparities can be attributed to structural inequities related to the STEM pipeline. Therefore, to mitigate these disparities, informal STEM education programs can help increase participation in STEM and create more opportunities for Black women and girls. Thus, this collective case study addressed the following research questions: 1. How do adolescent Black girls engage in and respond to informal STEM education programs? 2. How can informal STEM education programs develop adolescent Black girls' STEM identity and increase participation in STEM? 3. What pedagogical practices effectively engage adolescent Black girls in STEM? Purposeful criterion sampling was used to recruit participants for this study. The research process included interviewing four adolescent Black girls who attended informal STEM education programs. To understand each program's process and climate, participants described their experiences through initial questionnaires, interviews, and written prompt responses. Furthermore, this research used critical race feminism and Black feminist thought to analyze five prominent themes from the data. The findings suggest that Black girls who participate in informal STEM education programs (a) benefited from an affirming environment, (b) experienced engaging instructional strategies, (c) utilized support systems, (d) recognized racial representation was significant, and (e) experienced racial microaggressions. In addition, the findings support equitable STEM access for Black girls through informal education programs. The implications of this study also suggest a need to attend to the psychological and emotional needs of Black girls in informal STEM spaces.
In this study, the performance of the Lambda Coefficient of Rater-Mediated Agreement was evaluated with other chance-corrected agreement coefficients. Lambda is grounded in rater-mediated assessment theory and was developed as an alternative to Kappa (Cohen, 1960) and other chance-corrected agreement coefficients. Lambda has two variations, a general form that is calculated similarly to how most chance-corrected agreement coefficients are calculated, such as Kappa (Lambert et al., 2021). The general form of Lambda is referred to as Lambda-1. Lambda-2 differs from Lambda-1 in the calculation of the proportion of expected chance agreement. Lambda-2 uses known population proportions when available and applies those proportions in the calculation of expected chance agreement. In total, six coefficients were calculated using generated data by varying the amount and location of agreement and disagreement between ratings across two-, three-, and four-point rating scales. The exact agreement specifications ranged from 75% to 95% across 135 planned data conditions. The simulations adjusted prevalence indices according to exact agreement specifications (Xie, 2013). Results demonstrated the robustness of Lambda-1 and Lambda-2 to data conditions that are problematic for other coefficients. Both variations of Lambda produced benchmark agreement results that maintained meaning that may be diminished by other coefficients.
Based on Aristotle’s philosophy that ethics can be taught, this study examines whether the behavior of licensed accounting professionals is influenced by increased ethics Continuing Professional Education (CPE) required by state licensing boards. Since states are the licensing and oversight bodies of Certified Public Accountants (CPAs), it is important to know what actions state boards have taken to help increase the public trust of the accounting profession, and the efficacy of those actions.
States were selected based on whether they required ethics CPE hours and whether this changed during the study period 2008-2019. Fourteen states were selected (46% of U.S. CPAs). Data was obtained from publicly available resources on the individual state boards’ websites. 7,969 sanctions were hand coded, scored and analyzed for 3 variables: the sanctions rate, severity of acts conducted by CPAs and the severity of sanctions assessed on CPAs.
The results of this study identified inconsistencies in the monitoring and oversight activities of state licensing boards and the variability of publicly available information. Findings suggest that the number of sanctions may be more affected by the level of oversight of state boards of accountancy, driven by resources available to them, than the behavior of regulated accounting professionals.
Resilience research within the field of entrepreneurship has increasingly received attention from academia. However, most studies have considered this construct under extreme circumstances such as war, the aftermath of natural disasters, and economic crisis. This dissertation examines resilience from an entrepreneur's perspective by examining the role that culture plays in the consequence of venture performance. Drawing from acculturation theory, this dissertation considers cultural distance, cultural conflict, and perceived discrimination of the entrepreneurs as moderating variables in the interaction between resilience and venture performance. A sample of entrepreneurs (N=158) provides insights into these interactions. Even though this study did not find support to suggest such relationships or moderating effects, it recommends possible improvements and future research agenda in cross-disciplinary studies within the field of entrepreneurship.
Solar-plus-storage systems provide efficient energy yield and management, resilience, and more revenue to residential houses and buildings. In solar-plus-storage systems, power electronics converters are integral components to generate the maximum output power from a solar photovoltaic (PV) array, store the generated energy into a battery, and finally deliver and manage the power to an electric load or the electric grid. Many existing solar-plus-storage systems still use and combine legacy power electronics convert topologies initially designed for a solar PV generation system or a battery energy storage system (BESS) separately. These power converters are connected in parallel to an AC or DC point of common coupling: AC-parallel and DC-parallel integration methods. Another integration method of a solar-plus-storage system is connecting a high-voltage battery to the high-voltage DC bus in parallel in a solar PV generation system having two-stages power converter architecture: In-line integration method. These methods result in increased costs and size, lower energy yields due to the increased number of power electronics converters, and the requirement of high-voltage PV strings and batteries.
This research studies new PV-battery integration methods and develops PV-battery series optimizers—power electronics converters optimally designed for different residential solar-plus-storage systems. The two PV-battery integration methods are proposed: AC-series integration and DC-series integration. The proposed integration methods are based on the series connection of PV and battery modules. The AC-series integration method assists the residential panel-level series-connected solar PV inverters in reducing the intermittent PV output fluctuations with a low-voltage-profile battery energy storage inverter. The DC-series integration enables PV voltage support, reducing the number of power converter stages, reducing the rated power of power converters, improving the system round-trip efficiency, and seamless source integration. Three PV-battery series optimizers are developed for different solar-plus-storage applications. The proposed power converter topologies and controls are discussed in this dissertation. Off-line simulation, real-time controller hardware-in-the-loop simulation, and lab-scale experiment results are included and analyzed to demonstrate the operating and design principle and the control performance of the proposed system.
CNC machining is a critical manufacturing technology in effectively all modern products. Any improvement in efficiency or automation that reduces the cost of CNC machining is of tremendous value to the manufacturing industry. One of the most time-consuming steps in CNC machining, especially in a high-mix low-volume scenario, such as prototyping, is the current tool path planning workflow. The current industrial state of Computer-Aided Manufacturing (CAM) tools used to generate toolpaths requires highly trained CNC programmers. Typically, programmers manually select the features to be machined, the tools to use for each feature, the specific tool paths topology, and the feeds and speeds.
In the research community, there is a lot of focus on the automation of the tool path planning process, aiming to reduce the significant effort required to generate toolpaths. Researchers have developed novel feature recognition techniques, automated tool path generation methods, and tool selection algorithms. However, these methods all come with certain caveats and limitations. Some only work on continuous geometries. Others only work on certain feature types.
This dissertation introduces a feature based automated tool path planning system with the focus on implementing robust and generalized algorithms that work on arbitrary geometries with the full range of features based on discrete geometry. Support for discrete geometry is valuable because there are many situations where only discrete geometry is available as in models generated from 3D scanning systems. Specifically, a robust region segmentation technique is developed to simplify machining feature recognition from discrete geometry. Once the features are recognized, an automated optimal cutter set selection approach aiming at a minimum machining time is proposed to improve the machining efficiency for arbitrary features. Additionally, an automated deburring tool path planning method is introduced to eliminate the manual edge deburring and specifically to work with 3D discrete geometry. With the robust and automated algorithms as a solid foundation, a fully automated tool path planning system with limited human interactions is built and demonstrated on a series of parts with complex intersecting features. The net result is a complete 3D CAM process that goes from geometry to G-code in less than 1 minute.
Recent technological advances have facilitated the use of mobile robots for a wide range of coverage applications such as inspection and mapping of infrastructure, precision agriculture, and disaster management. With the proliferation of these tasks comes an increasing need for autonomous systems to efficiently gather data pertinent for analyzing the state of the environment. The dissertation answers the following fundamental question: How should resource-constrained robots traverse the environment to collect data from all the relevant features? These features of interest can be represented as points, lines or curves, and areas. This dissertation unifies simultaneous coverage of all three types of features into a novel generalized coverage framework, develops algorithms for efficient coverage using multiple mobile robots, and validates them in experiments.
The dissertation comprehensively studies the line coverage problem, i.e., coverage of one-dimensional features, which lays the foundation of the generalized coverage problem. We develop algorithms to transform point and area features into linear features and use line coverage algorithms to solve generalized coverage efficiently. The algorithms substantially improve the state of the art while incorporating battery life constraints, nonholonomic constraints for robots that cannot take turns in place, and multiple home locations for large-scale environments.
The gap in supply (i.e., shortage) and demand of the STEM workforce have prompted extensive research on identifying factors that predict STEM outcomes and retention of students. Few studies, however, have examined the relationships between STEM outcomes and predictors in an integrated model, taking into account measurement errors in the predictors. Drawing upon the Expectancy-Value Model of Achievement Related Performance and Choice, I conducted a structural equation modeling (SEM) analysis to examine the relationships among academic support, academic engagement, mathematics readiness, and hours worked and first-year STEM students’ academic success and retention. The SEM allowed me to investigate the relationships between predictors and outcomes simultaneously while accounting for the measurement error. The sample consisted of first-year STEM majors who took the National Survey of Student Engagement during 2016, 2018, and 2020 academic years. Results indicated that academic support was a statistically significant predictor of first-year STEM students’ academic success and retention. Additionally, mathematics readiness was found to be a statistically significant predictor of first-year retention. Last but not the least, results suggested that female students on average were more likely than their male counterparts to engage in academic support and academic engagement activities even though females had longer on-campus work hours than males. These results have implications for policies and practices aimed at improving STEM retention. Areas of further research are also identified.
