Reference functional model for the activities and processes involved in production (e.g., designing, manufacturing, and recovering products/materials) in a circular economy. The model includes inputs and outputs of each activity, as well as factors that constrain and enable the activities (controls and mechanisms), using the IDEF0 methodology. It provides a baseline scenario that is relevant for stakeholders across product development and supply chain, including business strategists, material suppliers, designers, manufacturers, consumers, and recovers/recyclers.

The parsers featured in this module are constructed to parse LAMMPS output files, (i.e., dump files), containing data for given potential energy values (an approximation of the Hamiltonian), U_{𝞴,𝞴'}, at specified values of 𝞴 and 𝞴', where varies from one to zero scaling the potential between solute and solvent in a reversible manner. The system is equilibrated at a 𝞴-state and energy values are evaluated at alternative 𝞴 values denotes by, 𝞴'. Because generating the input files can be cumbersome, functions have been included to generate the appropriate sections.

ETSpy is a HyperSpy extension package package for the processing, aligment, and reconstruction of electron tomography data from TEM/STEM instruments. Tools are provided for basic tilt series data processing, stack alignment, and reconstruction using the ASTRA Toolbox.

Sim-PROCESD is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices/components that can be configured by the user to model various real-world manufacturing systems.

This dataset contains data used in the investigation of Universally Unique Identifiers (UUIDs) that will enable a standards-based digital thread of product data in ISO 10303-242. Included are EXPRESS schema used for implementation and .stp files that were exported from native CAD (CATIA V5, Creo, and NX). UUIDs were assigned to CAD features during .stp export for each of the four design iterations.

The main goal of `pyproject2conda` is to provide a means to keep all basicdependency information, for both `pip` based and `conda` based environments, in`pyproject.toml`. I often use a mix of pip and conda when developing packages,and in my everyday workflow. Some packages just aren't available on both. The application provides a simple comment based syntax to add information to dependencies when creating `environment.yaml`. This package is actively used by the author, but is still very much a work inprogress.

The purpose of this software and data is to enable reproduction and facilitate extension of the computational results associated with the following referenceDeJaco, R. F.; Majikes, J. M.; Liddle, J. A.; Kearsley, A. J. Binding, Brightness, or Noise? Extracting Temperature-dependent Properties of Dye Bound to DNA. Biophysical Journal, 2023, https://doi.org/10.1016/j.bpj.2023.03.002.The software and data can also be found at https://github.com/usnistgov/dye_dna_plates.

An experiment was set up within a machine tool at the National Institute of Standards and Technology (NIST) to test vision-based thermal drift tracking methods. A wireless microscope within a tool holder in the spindle is used to capture videos of image targets attached to the worktable. For each target, one video is captured during spindle rotation orthogonal to the worktable and another video is captured during axis translation orthogonal to the worktable.

Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given an parametric model - analogous to a fitting function - Bayesian inference uses each measurement 'data point' to refine model parameters. Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties. A TCP socket interface allows the software to be used from experimental control software written in other programming languages.

`analphipy` is a python package to calculate metrics for classical models for pair potentials. It provides a simple and extendable api for pair potentials creation. Several routines to calculate metrics are included in the package. The main features of `analphipy` are 1) Pre-defined spherically symmetric potentials. 2) Simple interface to extended to user defined pair potentials. 3) Routines to calculate Noro-Frenkel effective parameters. 4) Routines to calculate Jensen-Shannon divergence.