This capstone report presents E.D.E.N. (Every Day, Every Night) — an original continuous improvement framework designed for nonstop, high-dependency operations in industries such as logistics, aviation, digital infrastructure, and healthcare. Drawing on principles from Lean, Six Sigma, high-reliability organizations (HROs), and data-driven decision science, the paper introduces four interlocking pillars: Engaged & Empowered Teams, Data-Driven Continuous Feedback, End-to-End Alignment, and Nonstop Adaptive Resilience. Through detailed analysis of recent global disruptions — including the 2025 CrowdStrike outage, Boeing’s manufacturing failures, the Red Sea shipping crisis, and Taiwan’s semiconductor challenges — the work demonstrates how organizations can embed real-time adaptability, resilience, and continuous improvement directly into their operations. The E.D.E.N. framework is proposed as a new model for achieving operational excellence and resilience in an era where downtime is no longer an option.
By considering various depictions of the Adoration of the Magi between 1400 to 1485, I establish that the portrayal of the Magus within The Master of the Legend of Saint Lucy’s painting directly reflects the economic realities in Bruges throughout the same period. In turn, my close examination of marketplace standards alongside themes of pilgrimage, shifting racial ideals, and a growing interest in secular subjects, offers new insight into 15th century adaptations of the Adoration.
The intent of this design is to increase vegetative coverage by at least 50% to capture more rainfall, improve urban heat island effect, and improve aesthetic value. As well as to diversify plant pallet for an increase in biodiversity as well as aesthetic improvement, to ensure that the new bed designs still allow access to equipment that are necessary for daily operation, and to ensure that the bed designs are accessible to all.
A study of nonprofit administration, using the organization Every Child Succeeds as an example.
Chasing Success follows the first twenty years of the organization Every Child Succeeds under the leadership of their former President turned author, Judith Van Ginkel. Every Child Succeeds is a regional nonprofit located in Cincinnati, Ohio that focuses on home visitation and support for parents from pregnancy through the first one thousand days of their newborn's life. The organization was born in the 1990s out of widespread scientific evidence about the impacts of early childhood on development across the lifespan.
Chasing Success uses the story of Every Child Succeeds as a case study for readers interested in the changing landscape of nonprofit administration. With the benefit of Van Ginkel's years of experience in nonprofit management, this book offers concrete lessons about developing a new nonprofit, utilizing research and best practices, learning to be adaptable, and being accountable to stakeholders. Van Ginkel also explores how changing policies and funding priorities for larger national nonprofits and the state and federal governments can impact how regional nonprofits work to achieve their missions, an often underappreciated and under-discussed reality for many smaller organizations around the country.
An expanded version of "The Future of Conflict: Neurowarfare", both of which discuss emerging neurotechnology, neuroscience, and their implications for war, politics, medicine, ethics, and society.
An overview of Walter A. McDougall's 1997 book "Promised Land, Crusader State: The American Encounter with the World Since 1776" with some concluding thoughts.
Hyperelastic constitutive models of soft tissue mechanical behavior are extensively used in applications like computer-aided surgery, injury modeling, etc. While numerous constitutive models have been proposed in the literature, an objective method is needed to select a parsimonious model that represents the experimental data well and has good predictive capability. This is an important problem given the large variability in the data inherent to soft tissue mechanical testing.
In this work, we discuss a Bayesian approach to this problem based on Bayes factors. We propose a holistic framework for model selection, wherein we consider four different factors to reliably choose a parsimonious model from the candidate set of models. These are the qualitative fit of the model to the experimental data, evidence values, maximum likelihood values, and the landscape of the likelihood function. We consider three hyperelastic constitutive models that are widely used in soft tissue mechanics: Mooney-Rivlin, Ogden and exponential. Three sets of mechanical testing data from the literature for agarose hydrogel, bovine liver tissue, porcine brain tissue are used to calculate the model selection statistics. A nested sampling approach is used to evaluate the evidence integrals. In our results, we highlight the robustness of the proposed Bayesian approach to model selection compared to the likelihood ratio, and discuss the use of the four factors to draw a complete picture of the model selection problem.
