Open Access. Powered by Scholars. Published by Universities.®
- Keyword
- Publication
- Publication Type
Articles 1 - 5 of 5
Full-Text Articles in Engineering
System Dynamics Modeling For Traumatic Brain Injury: Mini-Review Of Applications, Erin S. Kenzie, Elle L. Parks, Nancy Carney, Wayne Wakeland
System Dynamics Modeling For Traumatic Brain Injury: Mini-Review Of Applications, Erin S. Kenzie, Elle L. Parks, Nancy Carney, Wayne Wakeland
Systems Science Faculty Publications and Presentations
Traumatic brain injury (TBI) is a highly complex phenomenon involving a cascade of disruptions across biomechanical, neurochemical, neurological, cognitive, emotional, and social systems. Researchers and clinicians urgently need a rigorous conceptualization of brain injury that encompasses nonlinear and mutually causal relations among the factors involved, as well as sources of individual variation in recovery trajectories. System dynamics, an approach from systems science, has been used for decades in fields such as management and ecology to model nonlinear feedback dynamics in complex systems. In this mini-review, we summarize some recent uses of this approach to better understand acute injury mechanisms, recovery …
Learning From Machines: Insights In Forest Transpiration Using Machine Learning Methods, Morgan Tholl
Learning From Machines: Insights In Forest Transpiration Using Machine Learning Methods, Morgan Tholl
Dissertations and Theses
Machine learning has been used as a tool to model transpiration for individual sites, but few models are capable of generalizing to new locations without calibration to site data. Using the global SAPFLUXNET database, 95 tree sap flow data sites were grouped using three clustering strategies: by biome, by tree functional type, and through use of a k-means unsupervised clustering algorithm. Two supervised machine learning algorithms, a random forest algorithm and a neural network algorithm, were used to build machine learning models that predicted transpiration for each cluster. The performance and feature importance in each model were analyzed and compared …
Reducing Opioid Use Disorder And Overdose Deaths In The United States: A Dynamic Modeling Analysis, Erin J. Stringfellow, Tse Yang Lim, Keith Humphreys, Catherine Digennero, Celia Stafford, Elizabeth Beaulieu, Jack Homer, Wayne Wakeland, Multiple Additional Authors
Reducing Opioid Use Disorder And Overdose Deaths In The United States: A Dynamic Modeling Analysis, Erin J. Stringfellow, Tse Yang Lim, Keith Humphreys, Catherine Digennero, Celia Stafford, Elizabeth Beaulieu, Jack Homer, Wayne Wakeland, Multiple Additional Authors
Systems Science Faculty Publications and Presentations
Opioid overdose deaths remain a major public health crisis. We used a system dynamics simulation model of the U.S. opioid-using population age 12 and older to explore the impacts of 11 strategies on the prevalence of opioid use disorder (OUD) and fatal opioid overdoses from 2022 to 2032. These strategies spanned opioid misuse and OUD prevention, buprenorphine capacity, recovery support, and overdose harm reduction. By 2032, three strategies saved the most lives: (i) reducing the risk of opioid overdose involving fentanyl use, which may be achieved through fentanyl-focused harm reduction services; (ii) increasing naloxone distribution to people who use opioids; …
Growing Reservoir Networks Using The Genetic Algorithm Deep Hyperneat, Nancy L. Mackenzie
Growing Reservoir Networks Using The Genetic Algorithm Deep Hyperneat, Nancy L. Mackenzie
Student Research Symposium
Typical Artificial Neural Networks (ANNs) have static architectures. The number of nodes and their organization must be chosen and tuned for each task. Choosing these values, or hyperparameters, is a bit of a guessing game, and optimizing must be repeated for each task. If the model is larger than necessary, this leads to more training time and computational cost. The goal of this project is to evolve networks that grow according to the task at hand. By gradually increasing the size and complexity of the network to the extent that the task requires, we will build networks that are more …
Reconstructability Analysis: Discrete Multivariate Modeling, Martin Zwick
Reconstructability Analysis: Discrete Multivariate Modeling, Martin Zwick
Systems Science Faculty Publications and Presentations
An introduction to Reconstructability Analysis for the Discrete Multivariate Modeling course and for other purposes.