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.

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.

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.

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.

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.

The Cancer Moonshot Patent Data Set API allows the public to discover and search USPTO Cancer Moonshot Patent Data.

The Intellectual Property (IP) Marketplace Platform, also called Patents 4 Partnerships is an initiative of the U.S. Patent and Trademark Office (USPTO) that brings together those who have technologies and want to make them available for licensing and those who have an interest in and the ability to commercialize the technologies. The IP Marketplace Platform provides a voluntary listing of patents and patent application publications (pre-grant publications, pgpubs) indicated as *available for licensing* on external public websites or in the USPTO Patent Official Gazette Notices.

Registered patent practitioners are individuals who have passed the USPTO's registration exam and met the qualifications to represent patent applicants before the USPTO. Trademark practitioners are attorneys who are active members in good standing of the bar of the highest court of any State. NOTE: Because Trademark attorneys need not apply for registration to practice trademark law before the USPTO, the USPTO does not maintain a roster of trademark attorneys. The names of attorneys who specialize in trademark law may be found online, or by contacting a local bar association.

Offers exclusive access to patent application status information for unpublished patent applications only to the applicant/inventor or his/her representative(s).

The 24 Hour Box zipfile contains images of daily Trademark applications in either JPG format (black and white, grayscale, or color) or TIFF format (black and white) processed through the Trademark Image Capture and Retrieval System (TICRS) and viewable using any standard image viewer. JPG images are accompanied by an XML file that contains text information about the application. TIFF images contain the application number embedded in the image (stamped prior to scan).