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Critical infrastructures in many countries face the problem of aging and, thus, require significant upgrades to continue serving their purpose for the next few decades, especially in the face of extreme weather events caused by global climate change. Given the urgent need for such improvements and the substantial funding gaps being experienced, prioritizing investments in critical infrastructures is a challenging task for governments. Furthermore, the need to assure equitable solutions, as well as to consider deep uncertainty due to climate change, adds to the complexity of the problem. We seek to address this complexity by developing a set of models that explicitly consider both horizontal and vertical equity, along with efficiency, in prioritizing stormwater infrastructure improvement projects. While horizontal equity seeks to provide equal resources to everyone, vertical equity aims to allocate relatively more resources to vulnerable groups who are disproportionately susceptible to shocks and are more likely to fall into chronic poverty. By differentiating between losses in horizontal equity and vertical equity due to efficiency considerations, the models provide a practical approach to find the right balance among efficiency, horizontal equity, and vertical equity. The initial models are then extended into regret-based optimization models to help address the issue of deep uncertainty. A case study of stormwater infrastructure improvement in the City of Miami is presented, through which the performance of the models is explored both with and without the projected sea-level rise scenarios. The findings highlight the value of the proposed approach in promoting equity while maintaining efficiency.

While the complexities and challenges facing healthcare continue to grow, the focus on improving surgical practices remains constant. Possessing a strong influence over patient referral patterns, public reputation/prominence, and financial performance, surgical practices command heightened attention on operational performance and clinical outcomes. Executive leadership cannot support (nor improve) a surgical practice without comprehending the importance of team dynamics in the operating room (OR) environment. Previous literature offers mixed and incomplete results on themes of team familiarity and OR efficiency, frequently citing handoffs, late starts, and task disruptions as catalysts for negative performance. Studies routinely use historical interaction counts to measure team familiarity, which often neglect the degree of participation (engagement) across prior experiences. Similarly, counts of handoffs or individuals entering an OR do not offer an accurate assessment of team performance. Guided by historical studies, four hypotheses are presented and argue that enhancing surgical team dynamics yield favorable improvements for operational performance and clinical outcomes. Utilizing data from 9,049 neurologic surgery cases performed at two separate campuses (belonging to the same organization) over a three-year timeframe (March 2019 to November 2021), this study measures surgical team dynamics in a highly complex setting through the lens of case continuity and surgeon familiarity to assess key outputs: case scheduling errors (proxy for operational performance) and post-operative complications within 30-days of surgery (proxy for clinical outcomes)., 2023, Includes bibliography., Degree granted: Dissertation (PhD)--Florida Atlantic University, 2023., Collection: FAU Electronic Theses and Dissertations Collection

In health care organizations, the division of labor and a need for continuous, 24-hour treatment subjects patients to multiple transitions in care. These transitions, of "handovers" are potential points of failure that have seen very little study. We observed transitions of care in five hospital emergency departments as part of a larger study on safety in emergency care and found that in addition to many other differences in work patterns among the various hospitals, very different sorts of handovers occurred in different contexts, and these differences appeared to reflect a common structure. Using these observations, we have proposed a conceptual framework for characterizing handover events. The ability to characterize certain types of transitions may help to clarify future studies, while assisting in the development of interventions to better fit the context of clinical work.

Summary: This study looks at the database technique of data warehousing and data mining to analyze the business problems related to customer churn in the wireless industry. The customer churn due to new industry regulations has hit the wireless industry hard. The study uses data warehousing and data mining to model the customer database to predict churn rates and suggest timely recommendations to increase customer retention and thereby increase overall profitability. The Naive Bayes algorithm for supervised learning was the prediction algorithm used for data modeling in the study. The data set used in the study consists of one hundred thousand real wireless customers. The study uses database tools such as Oracle database with data mining options and JDeveloper for implementing the models. The data model developed with the calibration data was used to predict the churn for the wireless customers along with the predictive accuracy and probabilities of the results., College of Business, Thesis (M.B.A.)--Florida Atlantic University, 2003., Collection: FAU Electronic Theses and Dissertations Collection