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Additive manufacturing, or 3D printing, can create custom parts for electromagnetic devices on-demand and at a low cost. These devices are highly sensitive, and each component requires precise fabrication. Until recently, though, the only way to diagnose printing errors was to make, measure and test a device or to use in-line simulation, both of which are computationally expensive and inefficient.

To remedy this, a research team co-led by Penn State created a first-of-its-kind methodology for diagnosing printing errors with machine learning in real time. The researchers describe this framework—published in Additive Manufacturing —as a critical first step toward correcting 3D-printing errors in real time. According to the researchers, this could make printing for sensitive devices much more effective in terms of time, cost and computational bandwidth.

“A lot of things can go wrong during the additive manufacturing process for any component,” said Greg Huff, associate professor of electrical engineering at Penn State. “And in the world of electromagnetics, where dimensions are based on wavelengths rather than regular units of measure, any small defect can really contribute to large-scale system failures or degraded operations. If 3D printing a household item is like tuning a tuba—which can be done with broad adjustments—3D-printing devices functioning in the electromagnetic domain is like tuning a violin: Small adjustments really matter.”

Engineers sometimes turn to nature for inspiration. Cold Spring Harbor Laboratory Associate Professor Saket Navlakha and research scientist Jonathan Suen have found that adjustment algorithms—the same feedback control process by which the Internet optimizes data traffic—are used by several natural systems to sense and stabilize behavior, including ant colonies, cells, and neurons.

Internet engineers route data around the world in small packets, which are analogous to ants. As Navlakha explains, “The goal of this work was to bring together ideas from machine learning and Internet design and relate them to the way ant colonies forage.”

The same algorithm used by internet engineers is used by ants when they forage for food. At first, the colony may send out a single ant. When the ant returns, it provides information about how much food it got and how long it took to get it. The colony would then send out two ants. If they return with food, the colony may send out three, then four, five, and so on. But if ten ants are sent out and most do not return, then the colony does not decrease the number it sends to nine. Instead, it cuts the number by a large amount, a multiple (say half) of what it sent before: only five ants. In other words, the number of ants slowly adds up when the signals are positive, but is cut dramatically lower when the information is negative. Navlakha and Suen note that the system works even if individual ants get lost and parallels a particular type of “additive-increase/multiplicative-decrease algorithm” used on the internet.

A series of experiments using paper airplanes reveals new aerodynamic effects, a team of scientists has discovered. Its findings enhance our understanding of flight stability and could inspire new types of flying robots and small drones.

“The study started with simple curiosity about what makes a good paper airplane and specifically what is needed for smooth gliding,” explains Leif Ristroph, an associate professor at New York University’s Courant Institute of Mathematical Sciences and an author of the study, which appears in the Journal of Fluid Mechanics. “Answering such basic questions ended up being far from child’s play. We discovered that the aerodynamics of how paper airplanes keep level flight is really very different from the stability of conventional airplanes.”

“Birds glide and soar in an effortless way, and paper airplanes, when tuned properly, can also glide for long distances,” adds author Jane Wang, a professor of engineering and physics at Cornell University. “Surprisingly, there has been no good mathematical model for predicting this seemingly simple but subtle gliding flight.”

Using a unique hydrogel, scientists in Saudi Arabia created a solar-driven system that successfully grows spinach by using water drawn from the air while producing electricity. The proof-of-concept design, described March 1 in the journal Cell Reports Physical Science, offers a sustainable, low-cost strategy to improve food and water security for people living in dry-climate regions.

“A fraction of the world’s population still doesn’t have access to clean water or green power, and many of them live in rural areas with arid or semi-arid climate,” says senior author Peng Wang, a professor of environmental science and engineering at the King Abdullah University of Science and Technology (KAUST). “Our design makes water out of air using clean energy that would’ve been wasted and is suitable for decentralized, small-scale farms in remote places like deserts and oceanic islands.”

The system, called WEC²P, is composed of a solar photovoltaic panel placed atop a layer of hydrogel, which is mounted on top of a large metal box to condense and collect water. Wang and his team developed the hydrogel in their prior research, and the material can effectively absorb water vapor from ambient air and release the water content when heated.

Homomorphic encryption is considered a next generation data security technology, but researchers have identified a vulnerability that allows them to steal data even as it is being encrypted.

“We weren’t able to crack homomorphic encryption using mathematical tools,” says Aydin Aysu, senior author of a paper on the work and an assistant professor of computer engineering at North Carolina State University. “Instead, we used side-channel attacks. Basically, by monitoring power consumption in a device that is encoding data for homomorphic encryption, we are able to read the data as it is being encrypted. This demonstrates that even next generation encryption technologies need protection against side-channel attacks.”

Homomorphic encryption is a way of encrypting data so that third parties cannot read it. However, homomorphic encryption still allows third parties and third-party technologies to conduct operations using the data. For example, a user could use homomorphic encryption to upload sensitive data to a cloud computing system in order to perform analyses of the data. Programs in the cloud could perform the analyses and send the resulting information back to the user, but those programs would never actually be able to read the sensitive data.

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