Versar, Inc. Selects Locus' EIM as its Standard Environmental Data Management System
News Jan 14, 2014
Versar, Inc. has selected Locus Technologies’ (Locus’) Environmental Information Management (EIM) software to be its preferred environmental data management system for the firm’s Performance Based Remediation (PBR) program for the U.S. Air Force (USAF).
Versar will take advantage of EIM’s ability to support analytical data compatible with Environmental Resources Program Information Management System (ERPIMS), the electronic system that the Air Force uses for validation and management of data collected from environmental projects at its bases. In addition, Versar will utilize the ERPIMS regulatory export feature and the EIM data validation module.
Versar is currently providing PBR services to the USAF under the 2009 Worldwide Environmental Restoration and Construction (WERC 09) contract through September 2020. The Versar Program, as both Prime contractor and Team subcontractor, presently includes nearly 200 contaminated sites at 15 Air Force bases in 10 different states across the U.S. The total value of the work (if all options are awarded) is approximately $110M; Versar is the Prime contractor with direct responsibility for 128 sites valued at $93M under three separate Task Orders (TOs) and is a Team subcontractor on a fourth TO.
“We are very proud Versar has determined that EIM has the robust and versatile functionality to meet the company’s data management requirements for its USAF PBR Program,” said Neno Duplan, President & CEO of Locus. “We are constantly striving to incorporate specific features into our software, such as the ERPIMS compatibility, that will make a big difference for our customers.”
Google has signed an agreement to join CERN's openlab program. openlab is a public-private partnership with companies and other research organizations to develop information and communication technology (ICT) solutions. Google wants to explore possibilities for joint research and development projects in cloud computing, machine learning, and quantum computing.
With machine learning systems now being used to determine everything from stock prices to medical diagnoses, it's never been more important to look at how they arrive at decisions. A new approach out of MIT demonstrates that the main culprit is not just the algorithms themselves, but how the data itself is collected.