(NIST: Gaithersburg, MD) -- Buildings in the U.S. are generally designed to withstand the usual suspects: rain, wind, snow, and the occasional earthquake. Abnormal events, such as gas explosions, vehicle impacts, or uncontrolled building fires, aren’t typically a consideration.

(NIST: Gaithersburg, MD) -- The Hollings Manufacturing Extension Partnership (MEP), a program of the U.S. Department of Commerce’s National Institute of Standards and Technology (NIST), has awarded roughly $400,000 to each of its MEP National Network Centers in every state and Puerto Rico.

(RationalStat: Wilmington, DE) -- The global additive manufacturing market is estimated at $12.7 billion in 2022 and is expected to grow at a significant CAGR of more than 18% over the forecast period of 2019-2028.

(Vision Engineering: New Milford, CT) -- Vision Engineering, a world-leading provider of innovative inspection, metrology, and digital 3D visualization solutions, announces the launch of a third generation of ergonomic optical stereo microscopes.

(Creaform: Lévis, QC, Canada) -- Creaform, a business unit of AMETEK Inc.

(AMETEK MOCON: Minneapolis) -- AMETEK MOCON, a global leader in permeation instruments, testing, and services, continues to expand its portfolio of permeation analyzers. The OX-TRAN 2/28 is now an instrument series with the introduction of the new HR model.

(KECO: Houston) -- As companies take steps to become more environmentally friendly, they must be aware that their carbon output affects climate change.

(NIST: Gaithersburg, MD) -- Do you know Benchy the Boat? NIST researchers certainly do, and the 3D-printing design is helping them make waves with a technique called vat photopolymerization.

(ORNL: Oak Ridge, TN) -- The U.S. Department of Energy’s Oak Ridge National Laboratory will team up with six of eight companies that are advancing designs and research and development for fusion power plants with the mission to achieve a pilot-scale demonstration of fusion within a decade.

When machine-learning models are deployed in real-world situations, perhaps to flag potential disease in X-rays for a radiologist to review, human users need to know when to trust the model’s predictions.