The Free Energy and Advanced Sampling Simulation Toolkit (FEASST) is a free, open-source, modular program to conduct molecular and particle-based simulations with flat-histogram Monte Carlo and molecular dynamics methods. It is a software written in C++ and python which is made publicly available to aid in reproducibility. It is also provided as a service to the scientific community in which there are few , if any, Monte Carlo programs that support flat histogram methods and advanced sampling algorithms. This software is expected to be updated frequently with new methods.

Source of the Refl1d program for modeling and analyzing neutron and X-ray reflectometry data, with the addition of distributed roughness capability.

Using a modified Thomas-Fermi approximation, we model a reference semiconductor system comprising a quasi-1D nanowire with a series of five depletion gates whose voltages determine the number of quantum dots (QDs), the charges on each of the QDs, as well as the conductance through the wire. The original dataset, QFlow lite, consists of 1 001 idealized simulated measurements with gate configurations sampling over different realizations of the same type of device.

Polymer Reference Interaction Site Model (PRISM) theory describes the equilibrium spatial-correlations of liquid-like polymer systems including melts, blends, solutions, block copolymers, ionomers, liquid crystal forming polymers and nanocomposites. Using PRISM theory, one can calculate thermodynamic (e.g., second virial coefficients, Flory-Huggins interaction parameters, potentials of mean force) and structural (eg., pair correlation functions, structure factors) information for these macromolecular materials.

A method was developed to reduce the point-based registration error by restoring the rigid body condition (RRBC method). Registration is the process of transforming one coordinate frame to another coordinate frame. The coordinate frame from which points are transformed is called the working frame and the coordinate frame to which points are transformed is called the destination frame. The RRBC method can be used to reduce the uncertainty of a hole location and thus, improve the success rate for insertion tasks.

There is often a large amount of maintenance data already available for use in Smart Manufacturing systems, but in a currently-unusable form: service tickets and maintenance work orders (MWOs). Nestor is a toolkit for using Natural Language Processing (NLP) with efficient user-interaction to perform structured data extraction with minimal annotation time-cost.

In this study, a rail of a linear axis testbed was intentionally degraded to various levels, affecting the carriage motion, which can be deduced with either an inertial measurement unit (IMU) or a laser-based reference system. The rail was degraded over a 10-cm-long region. Thus, the two trucks that move on the rail interact with this region of the rail, changing the translational and angular error motions of the carriage. To mechanically simulate spalling, a handheld grinder was used to wear the surface of the raceway groove of the rail.

Wireless technology is a key enabler of the vision of the future factory work-cell. Such work-cell will operate autonomously with a high degree of mobility enabled by wireless technology. This model describes the work-cell using the Systems Modeling Language (SysML). Using SysML the structural and parametric characteristics of the work-cell are described. Our model provides the architectural components and performance constraints of the work-cell in which wireless is used for a significant portion of connectivity. It identifies the structural components, interfaces, and data flows.

The dataset contains both the robot's high-level tool center position (TCP) health data and controller-level components' information (i.e., joint positions, velocities, currents, temperatures, currents). The datasets can be used by users (e.g., software developers, data scientists) who work on robot health management (including accuracy) but have limited or no access to robots that can capture real data. The datasets can support the:

This tool is used to implement Monte Carlo analysis, which uses probabilistic sensitivity analysis to account for uncertainty. This tool is developed to follow the simulation segment of ASTM E1369. This technique involves a method of model sampling. Specification involves defining which variables are to be simulated, the distribution of each of these variables, and the number of iterations performed. The software then randomly samples from the probabilities for each input variable of interest. Three common distributions that are used include triangular, normal, and uniform.