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The Launch Period

NASA and United Launch Alliance recently updated the mission’s launch period – the range of days the rocket can launch in order to reach Mars. It now spans from July 30 to Aug. 15.

The launch period opening changed from July 17 to 30 due to launch vehicle processing delays in preparation for spacecraft mate operations. Four days were also added to the previously designated Aug. 11 end of the launch period. NASA and United Launch Alliance Flight Teams were able to provide those extra days after final weights of both the spacecraft and launch vehicle became available, allowing them to more accurately calculate the propellant available to get Perseverance on its way.

There is no question that motivation is one of the hardest and yet important factors in life. It’s the difference between success and failure, goal-setting and aimlessness, well-being and unhappiness. And yet, why is it so hard to get motivated — or even if we do, to keep it up?

That is the question that scientists led by Professor Carmen Sandi at EPFL and Dr Gedi Luksys at the University of Edinburgh have sought to answer. The researchers worked off previous knowledge that told them two things: First, that people differ a lot in their capacity to engage in motivated behavior and that motivational problems like apathy are common in neurodegenerative and psychiatric disorders. Second, to target an area of the brain called the “nucleus accumbens”.

Sitting close to the bottom of brain, the nucleus accumbens has been the subject of a lot of research. The reason is that it was quickly found to be a major player in functions like aversion, reward, reinforcement, and motivation.

Oak Ridge National Laboratory researchers have developed artificial intelligence software for powder bed 3D printers that assesses the quality of parts in real time, without the need for expensive characterization equipment.

The software, named Peregrine, supports the “digital thread” being developed at ORNL that collects and analyzes data through every step of the manufacturing process, from design to feedstock selection to the print build to .

“Capturing that information creates a digital ‘clone’ for each part, providing a trove of data from the raw material to the operational component,” said Vincent Paquit, who leads advanced manufacturing data analytics research as part of ORNL’s Imaging, Signals and Machine Learning group. “We then use that data to qualify the part and to inform future builds across multiple part geometries and with multiple materials, achieving new levels of automation and manufacturing quality assurance.”