The data set includes source code that implements a PBPK template model applicable to PFAS. It includes data digitized from Kim et al. (2018), Kim et al. (2019), and Loccisano et al. (2012) used to show the capability of the template to replicate published PFAS PBPK models. The template model is described in a paper that is in review at the journal Toxicological Sciences.

The dataset includes source code that was used to perform analyses described in the manuscript "Evaluating Impact of Anatomical and Physiological Variability on Human Equivalent Doses Using PBPK Models" by Schacht et al.

Source Code for the manuscript "Characterizing Variability and Uncertainty for Parameter Subset Selection in PBPK Models" -- This R code generates the results presented in this manuscript; the zip folder contains PBPK model files (for chloroform and DCM) and corresponding scripts to compile the models, generate human equivalent doses, and run sensitivity analysis.

The data set includes source code that implements a PBPK model template that has been extended with features capable of implementing models for volatile organic compounds (VOCs). It also includes data from the U.S. EPA IRIS assessments for DCM (2011) and methanol (2013) and data from Sasso et al. (2013), Ramsey and Andersen (1984), and Yoon et al. (2007) used to show the ability of the template to replicate published VOC PBPK models. The extension of the model template is described in a paper that will be submitted to the journal Toxicological Sciences.

Contains values from pbpk models for each study on n-butanol effects. This dataset is associated with the following publication: Segal, D., A. Bale, L. Phillips, A. Sasso, P. Schlosser, C. Starkey, and S. Makris. Issues in Assessing the Health Risks of n-Butanol. JOURNAL OF APPLIED TOXICOLOGY. John Wiley & Sons, Ltd., Indianapolis, IN, USA, 40(1): 72-86, (2020).

Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.