When Dr. Shen-Shyang Ho looks at a graph, he sees more than abstract data points. In the dynamic graphs he studies, the computer science researcher sees complex networks that change over time. An associate professor in the Department of Computer Science in Rowan University's College of Science & Mathematics, Ho has studied and developed machine-learning technologies for detecting anomalies in various application domains for nearly 20 years. "Anomalies are deviations from the normal," explained Ho, who also coordinates Rowan's master's degree program in computer science. Many anomalies are undesirable, he added, such as financial fraud, suspicious behavior, manufacturing defects and abnormal findings on medical tests.

Optical metasurfaces, ultrathin interfaces made up of uniform nanoscale structures, change the behavior of light waves hitting them to produce effects ranging from unique reflection and transmission properties to lens distortion removal. However, due to the small size of metasurface features, manufacturing defects can significantly reduce performance -- and they are hard to anticipate. A team of Penn State researchers developed a method to account for the effect of small defects before they've occurred to enable designs that can withstand these performance reductions. They published their approach in Nanophotonics in November. "With modern nanofabrication technology, superfine features -- or small structures inside metasurface components -- can be made consistently, but this can affect the processing time," said Ronald Jenkins, an electrical engineering doctoral candidate and first author on the paper.

Agtech capabilities are bringing traditional farming into the 21st century. These solutions range from sprawling LED-equipped indoor farming facilities to robotically plucking ripe produce off the vine using computer vision and artificial intelligence (AI). On Thursday, John Deere and Intel announced a pilot program that relies on AI and computer vision to detect defects in manufacturing related to the welding process. "Welding is a complicated process. This AI solution has the potential to help us produce our high-quality machines more efficiently than before," said Andy Benko, quality director at John Deere Construction and Forestry Division.

According to several studies by Intel spanning 2018, 2019, and 2020, AI and edge computing make it possible to positively identify up to 99% of visible manufacturing defects before a product ever leaves the line. "One of the most important things manufacturers care about is product quality," says Brian McCarson, Vice President and Senior Principal Engineer, Internet of Things Group (IOTG) at Intel Corporation and a featured speaker at Transform, VentureBeat's upcoming digital conference. "Manufactures prefer throwing away fewer defective products. They strive to have less rework and fewer customer returns. They also want to reduce the cost of their operations by making their tools and processes more efficient, and improve the reliability of their machines so they can proactively do maintenance before it is too late and have more predictable uptime."

Nasa has been forced to try and clarify the details of a leak on the International Space Station amid reports that it was made on purpose. The Russian space agency has been clear that it believes that the hole in the space station that caused the leak was not a "manufacturing defect". But Nasa said that while that is true, it did not necessarily mean that it had been done on purpose, as some local reports had suggested. It came amid conspiracy theories reported in Russian media that a Nasa astronaut had intentionally made the hole. Some reported it had been done to trigger an emergency alert on board the space station and allow the crew to come down for free – without that alert, Nasa would have to pay the Russian space agency.