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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.”

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.

More recently, digital twins have been the focus of a European Union-funded project that seeks to clone a patient’s entire brain. Dubbed Neurotwin, the research project aims to create virtual models that can be used to predict the effects of stimulation for the treatment of neurological disorders—including epilepsy and Alzheimer’s disease. When it comes to epilepsy, non-invasive stimulations (where electrical currents are painlessly delivered to the brain) have proven effective in tackling seizures. Given how drugs don’t help a third of epilepsy patients, the technology is coveted yet needs refinement. This is where virtual clones come in.

“The digital avatar is essentially a mathematical model running on a computer,” Giulio Ruffini, coordinator of the Neurotwin project, told WIRED. Including a network of embedded “neural mass models,” the technology hopes to create a map of the neural connections in the brain—a concept termed as the ‘connectome’. “In the case of epilepsy, some areas of the connectome could become overexcited,” the outlet mentioned. “In the case of, say, stroke, the connectome might be altered.” Once the digital clone has been created by the team, with about half an hour-worth of magnetic resonance imaging (MRI) data and ten minutes of electroencephalography (EEG) readings to capture electrical activities and realistically simulate the brain’s main tissues (including the scalp, skull, cerebrospinal fluid, and grey and white matter), it can then be used to optimise stimulation of the real patient’s brain.

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You’re not bad at math. Like pearls before swine, your beautiful brain is just far too complex for such basic things. property= description.

Two recent collaborations between mathematicians and DeepMind demonstrate the potential of machine learning to help researchers generate new mathematical conjectures.

Fundamental constants like e and π are ubiquitous in diverse fields of science, including physics, biology, chemistry, geometry, and abstract mathematics. Nevertheless, for centuries new mathematical formulas relating fundamental constants are scarce and are usually discovered sporadically by mathematical intuition or ingenuity.

Our algorithms search for new mathematical formulas. The community can suggest proofs for the conjectures or even propose or develop new algorithms. Any new conjecture, proof, or algorithm suggested will be named after you.

Pushing the boundaries of math requires great minds to pose fascinating problems. What if a machine could do it? Now, scientists created one that can.

It may look like a bizarre bike helmet, or a piece of equipment found in Doc Brown’s lab in Back to the Future, yet this gadget made of plastic and copper wire is a technological breakthrough with the potential to revolutionize medical imaging. Despite its playful look, the device is actually a metamaterial, packing in a ton of physics, engineering, and mathematical know-how.

It was developed by Xin Zhang, a College of Engineering professor of mechanical engineering, and her team of scientists at BU’s Photonics Center. They’re experts in metamaterials, a type of engineered structure created from small unit cells that might be unspectacular alone, but when grouped together in a precise way, get new superpowers not found in nature. Metamaterials, for instance, can bend, absorb, or manipulate waves—such as electromagnetic waves, sound waves, or radio waves. Each unit cell, also called a resonator, is typically arranged in a repeating pattern in rows and columns; they can be designed in different sizes and shapes, and placed at different orientations, depending on which waves they’re designed to influence.

Metamaterials can have many novel functions. Zhang, who is also a professor of electrical and computer engineering, biomedical engineering, and materials science and engineering, has designed an acoustic metamaterial that blocks sound without stopping airflow (imagine quieter jet engines and air conditioners) and a magnetic metamaterial that can improve the quality of magnetic resonance imaging (MRI) machines used for medical diagnosis.

Can you crumple up two sheets of paper the exact same way? Probably not—the very flexibility that lets flexible structures from paper to biopolymers and membranes undergo many types of large deformations makes them notoriously difficult to control. Researchers from the Georgia Institute of Technology, Universiteit van Amsterdam, and Universiteit Leiden have shed new light on this fundamental challenge, demonstrating that new physical theories provide precise predictions of the deformations of certain structures, as recently published in Nature Communications.

In the paper, Michael Czajkowski and D. Zeb Rocklin from Georgia Tech, Corentin Coulais from Universiteit van Amsterdam, and Martin van Hecke of AMOLF and Universiteit Leiden approach a highly studied exotic elastic material, uncover an intuitive geometrical description of the pronounced—or nonlinear—soft deformations, and show how to activate any of these deformations on-demand with minimal inputs. This new theory reveals that a flexible mechanical structure is governed by some of the same math as electromagnetic waves, phase transitions, and even black holes.

“So many other systems struggle with how to be strong and solid in some ways but flexible and compliant in others, from the human body and micro-organisms to clothing and industrial robots,” said Rocklin. “These structures solve that problem in an incredibly elegant way that permits a single folding mechanism to generate a wide family of deformations. We’ve shown that a single folding mode can transform a structure into an infinite family of shapes.”