Lifeboat Foundation

Stellarators, twisty machines that house fusion reactions, rely on complex magnetic coils that are challenging to design and build. Now, a physicist at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) has developed a mathematical technique to help simplify the design of the coils, making stellarators a potentially more cost-effective facility for producing fusion energy.

“Our main result is that we came up with a new method of identifying the irregular magnetic fields produced by stellarator coils,” said physicist Caoxiang Zhu, lead author of a paper reporting the results in Nuclear Fusion. “This technique can let you know in advance which coil shapes and placements could harm the plasma’s magnetic confinement, promising a shorter construction time and reduced costs.”

Fusion, the power that drives the sun and stars, is the fusing of light elements in the form of plasma—the hot, charged state of matter composed of free electrons and atomic nuclei—that generates massive amounts of energy. Twisty, cruller-shaped stellarators are an alternative to doughnut-shaped tokamaks that are more commonly used by scientists seeking to replicate fusion on Earth for a virtually inexhaustible supply of power to generate electricity.

This could lead to self-healing cars.

Researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a mathematical framework that can turn any sheet of material into any prescribed shape, inspired by the paper craft termed kirigami (from the Japanese, kiri, meaning to cut and kami, meaning paper).

Unlike its better-known cousin origami, which uses folds to shape paper, kirigami relies on a pattern of cuts in a flat paper sheet to change its flexibility and allow it to morph into 3D shapes. Artists have long used this artform to create everything from pop-up cards to castles and dragons.

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A trio of physicists from the National Autonomous University of Mexico and Tec de Monterrey has solved a 2,000-year-old optical problem—the Wasserman-Wolf problem. In their paper published in the journal Applied Optics, Rafael González-Acuña, Héctor Chaparro-Romo, and Julio Gutiérrez-Vega outline the math involved in solving the puzzle, give some examples of possible applications, and describe the efficiency of the results when tested.

Over 2,000 years ago, Greek scientist Diocles recognized a problem with optical lenses—when looking through devices equipped with them, the edges appeared fuzzier than the center. In his writings, he proposed that the effect occurs because the lenses were spherical—light striking at an angle could not be focused because of differences in refraction. Isaac Newton was reportedly stumped in his efforts to solve the problem (which became known as spherical aberration), as was Gottfried Leibniz.

In 1949, Wasserman and Wolf devised an analytical means for describing the problem, and gave it an official name—the Wasserman-Wolf problem. They suggested that the best approach to solving the problem would be to use two aspheric adjacent surfaces to correct aberrations. Since that time, researchers and engineers have come up with a variety of ways to fix the problem in specific applications—most particularly cameras and telescopes. Most such efforts have involved creating aspherical lenses to counteract refraction problems. And while they have resulted in improvement, the solutions have generally been expensive and inadequate for some applications.

We’re only a handful of months away from the year 2020, and with the way parts look and tech acts, it finally feels like we’re entering the future. It’s a future crafted by sophisticated 3D printers and machining centers, using materials provided by global-reaching supply chains and connected to an exponential rate of new superpowered gadgets. Nowadays, there’s really no reason to think any manufacturing feat is impossible. If something doesn’t exist, it’s just that we haven’t figured it out yet.

And this futuristic techtopia brimming with potential wouldn’t be possible if not for engineers—those dedicated, uber-creative folks plotting such a course, continuously improving the world around through the super power of… math.

Mathematics has been the indispensable fuel to make the impossible possible since at least the ancient Egyptians more than four thousand years ago. The Great Pyramid of Giza is the world’s oldest monument to its power. Amazingly, its geometrical elegance was calculated on papyrus scrolls, most of which have turned to dust long ago. Yet the universal language of math still speaks through its dimensions. And it will continue to do so for time immemorial.

Butterfly co-mimic pairs from the species Heliconius erato (odd columns) and Heliconius melpomene (even columns) sorted by greatest similarity (along rows, top left to bottom right) using machine learning.

J Hoyal Cuthill

A flamingo lives 40 years and a human being lives 90 years; a mouse lives two years and an elephant lives 60. Why? What determines the lifespan of a species? After analyzing nine species of mammals and birds, researchers at the Spanish National Cancer Research Center (CNIO) found a very clear relationship between the lifespan of these species and the shortening rate of their telomeres, the structures that protect the chromosomes and the genes they contain. The relationship is expressed as a mathematical equation, a formula that can accurately predict the longevity of the species. The study was done in collaboration with the Madrid Zoo Aquarium and the University of Barcelona.

“The telomere shortening rate is a powerful predictor of species lifespan,” the authors write in the prestigious journal Proceedings of the National Academy of Sciences (PNAS).

The study compares the telomeres of mice, goats, dolphins, gulls, reindeer, vultures, flamingos, elephants and humans, and reveals that species whose telomeres shorten faster have shorter lives.

A paper posted online this month has settled a nearly 30-year-old conjecture about the structure of the fundamental building blocks of computer circuits. This “sensitivity” conjecture has stumped many of the most prominent computer scientists over the years, yet the new proof is so simple that one researcher summed it up in a single tweet.

“This conjecture has stood as one of the most frustrating and embarrassing open problems in all of combinatorics and theoretical computer science,” wrote Scott Aaronson of the University of Texas, Austin, in a blog post. “The list of people who tried to solve it and failed is like a who’s who of discrete math and theoretical computer science,” he added in an email.

The conjecture concerns Boolean functions, rules for transforming a string of input bits (0s and 1s) into a single output bit. One such rule is to output a 1 provided any of the input bits is 1, and a 0 otherwise; another rule is to output a 0 if the string has an even number of 1s, and a 1 otherwise. Every computer circuit is some combination of Boolean functions, making them “the bricks and mortar of whatever you’re doing in computer science,” said Rocco Servedio of Columbia University.

I don’t use the term artificial gravity because, the gravity from a black hole is real.

If you have harnessed and are able to control a black hole would you be able to use it as portable gravity device?

I don’t really have the physics and the math to to figure it out. But it would seem that if you are in a low gravity environment, you could place a black hole under the floor, and have gravity. Presumably by changing the distance between the floor and the black hole you could adjust to 1 gravity or partial gravity.

Since its invention by a Hungarian architect in 1974, the Rubik’s Cube has furrowed the brows of many who have tried to solve it, but the 3D logic puzzle is no match for an artificial intelligence system created by researchers at the University of California, Irvine.

DeepCubeA, a deep reinforcement learning algorithm programmed by UCI computer scientists and mathematicians, can find the solution in a fraction of a second, without any specific domain knowledge or in-game coaching from humans. This is no simple task considering that the cube has completion paths numbering in the billions but only one goal state—each of six sides displaying a solid color—which apparently can’t be found through random moves.

For a study published today in Nature Machine Intelligence, the researchers demonstrated that DeepCubeA solved 100 percent of all test configurations, finding the shortest path to the goal state about 60 percent of the time. The algorithm also works on other combinatorial games such as the sliding tile puzzle, Lights Out and Sokoban.