Simantha 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 with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems.

A linear axis testbed at the Center for Intelligent Maintenance Systems (IMS Center) at the University of Cincinnati was run to failure (the detection of backlash) over one year with periodic data collected from an inertial measurement unit (IMU) on the carriage, two triaxial accelerometers on the ball nut, and the controller.

Source code associated with the Inverted Library Search Algorithm (ILSA) for identifying mixture components using is-CID mass spectra. This source code is frozen to accompany the manuscript titled "Updates to the Inverted Library Search Algorithm for mixture analysis" by Moorthy et. al.

This tool uses techniques from ASTM E3200 for evaluating manufacturing investments from the perspective of environmentally sustainable manufacturing by pairing economic methods of investment analysis with environmental aspect of manufacturing. The economic techniques used include net present value, internal rate of return, payback period, and hurdle rate. These four techniques are deterministic, meaning that they deal with known values that are certain. The tool also conducts a sensitivity analysis using Monte Carlo techniques.

On-resonance Rydberg atom-based radio-frequency (RF) electric field sensing methods remain limited by the narrow frequency signal detection bands available by resonant transitions. The use ofan additional RF tuner field to dress or shift a target Rydberg state can be used to return a detuned signal field to resonance and thus dramatically extend the frequency range available for resonantsensing.

This set corresponds to a (pending) publication, where we attempt to model spectral features appearing in the lab by calculating many segments of an inhomogeneous field. Every data set here is a transmission value, either normalized to 1 in modeled data, or an arbitrary-scaled voltage reading from a photodiode onto an oscilloscope. These are given in scans over coupling photon detuning, delta_C, which is divided by 2 pi, and given in MHz. Arrays are scans over delta_C, and position/fieldstrength, for figure 3.

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.

Python scripts and Python+Qt graphical user interface for calculating Feldman-Cousins confidence intervals for low-count Poisson processes in the presence of a known background and for Gaussian processes with a physical lower limit of 0.

The database contains radiative line-binned opacities for lanthanides (atomic number 57<=Z<=70) and actinides (atomic number 89<=Z<=102) which are of paramount importance for simulation of light curves and spectra produced by kilonovae. The opacities were calculated by the LANL group under the assumption of local thermodynamic equilibrium. The temperature grid cover the range from 0.01 eV to 5.0 eV while the mass density grid covers 16 orders of magnitude. The database provide search and selection tools along with graphical and tabular outputs.

SRM 2969 Electronic files containing certified values and their uncertainties, and the data used to assign those values