Through recent advancements in high-density dimensional (HDD) measurement technologies, such as 3D laser scanners, data-sets consisting of an almost complete representation of a manufactured part's geometry can now be obtained. While HDD data measurement devices have traditionally been used in reverse engineering application, they are beginning to be applied as in-line measurement devices. Unfortunately, appropriate quality control (QC) techniques have yet to be developed to take full advantage of this new data-rich environment and for the most part rely on extracting discrete key product characteristics (KPCs) for analysis. In order to maximize the potential of HDD measurement technologies requires a new quality paradigm. Specifically, when presented with HDD data, quality should not only be assessed by discrete KPCs but should consider the entire part being produced, anything less results in valuable data being wasted. This dissertation addresses the need for adapting current techniques and developing new approaches for the use of HDD data in manufacturing systems to increase overall quality control (QC) capabilities. Specifically, this research effort focuses on the use of HDD data for 1) Developing a framework for self-correcting compliant assembly systems, 2) Using statistical process control to detect process shifts through part surfaces, and 3) Performing automated part inspection for non-feature based faults. The overarching goal of this research is to identify how HDD data can be used within these three research focus areas to increase QC capabilities while following the principles of the aforementioned new quality paradigm. / Ph. D.

The COVID-19 pandemic, caused by SARS-CoV-2, has emerged as one of most significant health emergencies in recent history. SARS-CoV-2 has been characterized by the emergence of highly mutated variants that exhibit high transmissibility, virulence, and the capability of immune escape. The constantly evolving nature of the COVID-19 pandemic has underscored the necessity for a thorough comprehension of viral transmission dynamics, the effectiveness of novel monitoring techniques, and the determinants of health inequalities. This study explored several aspects of the pandemic, specifically emphasizing the emergence and dissemination of the Delta variant in Florida, the significance of environmental surveillance, and the factors associated with COVID-19 outcomes. Phylogenetic analysis using SARS-CoV-2 genome revealed that multiple independent introductions of the Delta variant fueled its spread within Florida. Further, we hypothesized that high-touch surface monitoring can be an alternative noninvasive approach to determine infection trend and detect variants. The study found high contamination rate on high-touch surfaces and the viral gene copy was positively correlated to the clinical cases in the university. Moreover, genome sequencing of environmental surface samples detected circulating and emerging variants. Additionally, spatial autocorrelation and regression analysis was conducted to investigate the relationship between county-level demographic, socioeconomic, and health-related factors and variation in COVID-19 cases, mortality, and case fatality rates. This study identified significant variations in COVID-19 outcomes across Florida counties, with factors such as age, obesity, rurality and importantly, vaccination rates playing key roles in explaining these disparities. Overall, this study emphasizes the importance of robust genomic surveillance for monitoring the emergence and spread of viral variants, the potential of environmental surface monitoring as a complementary public health tool, and the urgent need to address the underlying drivers of health disparities. These findings contribute to a more nuanced understanding of pandemic dynamics and inform data-driven strategies to mitigate the impact of future public health emergencies.

<p>Aquaplaning accidents are relatively rare, but could have fatal effects. The task of this master’s thesis is to use data from the Laser Road Surface Tester to detect road sections with risk of aquaplaning. </p><p>A three-dimensional model based on data from road surface measurements is created using MATLAB (version 6.1). From this general geometrical model of the road, a pond model is produced from which the theoretical risk ponds are detected. A risk pond indication table is fur-ther created. </p><p>The pond model seems to work well assuming that the data from the road model is correct. Determining limits for depth and length of risk ponds can be made directly by the user. MATLAB code is reasonably easy to understand and this leaves great opportunities for changing different parameters in a simple way. </p><p>Supplementary research is needed to further improve the risk pond detection model. Collecting data at smaller intervals and with more measurement points would be desirable for achieving better correlation with reality. In a future perspective, it would be wise to port the code to another programming language and this could make the computations faster.</p>

Aquaplaning accidents are relatively rare, but could have fatal effects. The task of this master’s thesis is to use data from the Laser Road Surface Tester to detect road sections with risk of aquaplaning. A three-dimensional model based on data from road surface measurements is created using MATLAB (version 6.1). From this general geometrical model of the road, a pond model is produced from which the theoretical risk ponds are detected. A risk pond indication table is fur-ther created. The pond model seems to work well assuming that the data from the road model is correct. Determining limits for depth and length of risk ponds can be made directly by the user. MATLAB code is reasonably easy to understand and this leaves great opportunities for changing different parameters in a simple way. Supplementary research is needed to further improve the risk pond detection model. Collecting data at smaller intervals and with more measurement points would be desirable for achieving better correlation with reality. In a future perspective, it would be wise to port the code to another programming language and this could make the computations faster.