Penn State officials and guests on Friday celebrated the start of construction for two new College of Engineering buildings on University Park’s West Campus, marking the beginning of a long-term plan to transform the facilities and footprint of the school’s largest academic program.
The “groundbreaking” ceremony, came well after the start of construction for the first of the buildings, counterintuitively called West 2, which began last September. But COVID-19 gathering restrictions were in place at the time and site work has only recently begun on the second building, dubbed West 1.
Located west of North Atherton Street and off of White Course Drive, the new buildings are the first phase of a
Penn State College of Engineering to host groundbreaking ceremony for new West Campus buildings
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Wireless sensors track sanitizer use, optimize replacement and refill operations
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Eligible Penn State student teams can apply for up to $500 in funding
Image: Penn State
Sean Yoder
February 23, 2021
UNIVERSITY PARK, Pa. Teams of entrepreneurial undergraduate Penn State students can apply for microgrants of up to $500 in seed money to help get their ideas off the ground.
The microgrants are provided through Lion LaunchPad and in cooperation with the Penn State College of Engineering and its School of Engineering Design, Technology, and Professional Programs’ (SEDTAPP) engineering entrepreneurship (E-SHIP) program. The funding can help student teams during the very early stages of their idea development and help prepare them to later compete in business competitions at Penn State, as well as enter programs such as the Happy Valley Launchbox FastTrack Accelerator, Summer Founder Program from Invent Penn State and TechCelerator through Ben Franklin Technology Partners.
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IMAGE: In an example shown with Old Main Penn State s main administration building on the University Park campus the researchers algorithm takes a simple image of the material microstructure. view more
Credit: Pranav Milind Khanolkar, Penn State
Various software packages can be used to evaluate products and predict failure; however, these packages are extremely computationally intensive and take a significant amount of time to produce a solution. Quicker solutions mean less accurate results.
To combat this issue, a team of Penn State researchers studied the use of machine learning and image colorization algorithms to ease computational load, maintain accuracy, reduce time and predict strain fields for porous materials. They published their work in the