Lifeboat Foundation

Composite membrane origami has been an efficient and effective method for constructing transformable mechanisms while considerably simplifying their design, fabrication, and assembly; however, its limited load-bearing capability has restricted its application potential. With respect to wheel design, membrane origami offers unique benefits compared with its conventional counterparts, such as simple fabrication, high weight-to-payload ratio, and large shape variation, enabling softness and flexibility in a kinematic mechanism that neutralizes joint distortion and absorbs shocks from the ground. Here, we report a transformable wheel based on membrane origami capable of bearing more than a 10-kilonewton load. To achieve a high payload, we adopt a thick membrane as an essential element and introduce a wireframe design rule for thick membrane accommodation. An increase in the thickness can cause a geometric conflict for the facet and the membrane, but the excessive strain energy accumulation is unique to the thickness increase of the membrane. Thus, the design rules for accommodating membrane thickness aim to address both geometric and physical characteristics, and these rules are applied to basic origami patterns to obtain the desired wheel shapes and transformation. The capability of the resulting wheel applied to a passenger vehicle and validated through a field test. Our study shows that membrane origami can be used for high-payload applications.

Origami has been a rich source of inspiration for art, education, and mathematics, and it has proven to be an efficient and effective method for realizing transformable structures in nature (1–3) and artificial systems (4–8). Composite membrane origami, the design technique based on the laminar composition of flexible membranes with rigid facet constraints, opens a new field for robotics by the transition from component assembly to lamination, which considerably simplifies design, fabrication, and assembly. This transition simplifies and speeds up fabrication and enables reaching size scales that were difficult to access before (9, 10). In addition, membrane origami provides a versatile shape-changing ability that has been exploited in various applications (11–15), and its applicability has been extended by additional design dimensions obtained from material characteristics such as softness and stretchability (16–19).

Beyond the aforementioned benefits, origami has been an effective design tool for constructing a high payload-to-weight structure, such as a honeycomb panel, by markedly increasing the buckling strength using unique geometric configurations (20, 21). Combining this feature with reconfigurability, various stiffness transition mechanisms have also been introduced (22–24). The rigidity of components is another important factor to secure high load capacity and closely related to the thickness. Origami design is, traditionally, a matter of organizing fold lines under fundamental and ideal assumptions—zero facet thickness and zero fold line width (25–27). However, in response to growing interest in origami-inspired applications that require load-bearing capability, various thickness accommodation methods have been introduced (28–30).

Classical hydrodynamics laws can be very useful for describing the behavior of systems composed of many particles (i.e., many-body systems) after they reach a local state of equilibrium. These laws are expressed by so-called hydrodynamical equations, a set of mathematical equations that describe the movement of water or other fluids.

Researchers at Oak Ridge National Laboratory and University of California, Berkeley (UC Berkeley) have recently carried out a study exploring the hydrodynamics of a quantum Heisenberg spin-1/2 chain. Their paper, published in Nature Physics, shows that the spin dynamics of a 1D Heisenberg antiferromagnet (i.e., KCuF₃) could be effectively described by a dynamical exponent aligned with the so-called Kardar-Parisi-Zhang universality class.

“Joel Moore and I have known each other for many years and we both have an interest in quantum magnets as a place where we can explore and test new ideas in physics; my interests are experimental and Joel’s are theoretical,” Alan Tennant, one of the researchers who carried out the study, told Phys.org. “For a long time, we have both been interested in temperature in quantum systems, an area where a number of really new insights have come along recently, but we had not worked together on any projects.”

Despite finding no specific examples, researchers have proved the existence of a pervasive kind of prime number so delicate that changing any of its infinite digits renders it composite.

During the pandemic, the National Museum of Mathematics found new ways to build human connections.

Artificial microswimmers that can replicate the complex behavior of active matter are often designed to mimic the self-propulsion of microscopic living organisms. However, compared with their living counterparts, artificial microswimmers have a limited ability to adapt to environmental signals or to retain a physical memory to yield optimized emergent behavior. Different from macroscopic living systems and robots, both microscopic living organisms and artificial microswimmers are subject to Brownian motion, which randomizes their position and propulsion direction. Here, we combine real-world artificial active particles with machine learning algorithms to explore their adaptive behavior in a noisy environment with reinforcement learning. We use a real-time control of self-thermophoretic active particles to demonstrate the solution of a simple standard navigation problem under the inevitable influence of Brownian motion at these length scales. We show that, with external control, collective learning is possible. Concerning the learning under noise, we find that noise decreases the learning speed, modifies the optimal behavior, and also increases the strength of the decisions made. As a consequence of time delay in the feedback loop controlling the particles, an optimum velocity, reminiscent of optimal run-and-tumble times of bacteria, is found for the system, which is conjectured to be a universal property of systems exhibiting delayed response in a noisy environment.

Living organisms adapt their behavior according to their environment to achieve a particular goal. Information about the state of the environment is sensed, processed, and encoded in biochemical processes in the organism to provide appropriate actions or properties. These learning or adaptive processes occur within the lifetime of a generation, over multiple generations, or over evolutionarily relevant time scales. They lead to specific behaviors of individuals and collectives. Swarms of fish or flocks of birds have developed collective strategies adapted to the existence of predators (1), and collective hunting may represent a more efficient foraging tactic (2). Birds learn how to use convective air flows (3). Sperm have evolved complex swimming patterns to explore chemical gradients in chemotaxis (4), and bacteria express specific shapes to follow gravity (5).

Inspired by these optimization processes, learning strategies that reduce the complexity of the physical and chemical processes in living matter to a mathematical procedure have been developed. Many of these learning strategies have been implemented into robotic systems (7–9). One particular framework is reinforcement learning (RL), in which an agent gains experience by interacting with its environment (10). The value of this experience relates to rewards (or penalties) connected to the states that the agent can occupy. The learning process then maximizes the cumulative reward for a chain of actions to obtain the so-called policy. This policy advises the agent which action to take. Recent computational studies, for example, reveal that RL can provide optimal strategies for the navigation of active particles through flows (11–13), the swarming of robots (14–16), the soaring of birds , or the development of collective motion (17).

The 21-digit solution to the decades-old problem suggests many more solutions exist.

What do you do after solving the answer to life, the universe, and everything? If you’re mathematicians Drew Sutherland and Andy Booker, you go for the harder problem.

In 2019, Booker, at the University of Bristol, and Sutherland, principal research scientist at MIT, were the first to find the answer to 42. The number has pop culture significance as the fictional answer to “the ultimate question of life, the universe, and everything,” as Douglas Adams famously penned in his novel “The Hitchhiker’s Guide to the Galaxy.” The question that begets 42, at least in the novel, is frustratingly, hilariously unknown.

Continue reading “After Cracking the ‘Sum of Cubes’ Puzzle for 42, Mathematicians Solve Harder Problem That Has Stumped Experts for Decades” »

Our sky is missing supernovas. Stars live for millions or billions of years. But given the sheer number of stars in the Milky Way, we should still expect these cataclysmic stellar deaths every 30–50 years. Few of those explosions will be within naked-eye-range of Earth. Nova is from the Latin meaning “new”. Over the last 2000 years, humans have seen about seven “new” stars appear in the sky – some bright enough to be seen during the day – until they faded after the initial explosion. While we haven’t seen a new star appear in the sky for over 400 years, we can see the aftermath with telescopes – supernova remnants (SNRs) – the hot expanding gases of stellar explosions. SNRs are visible up to a 150000 years before fading into the Galaxy. So, doing the math, there should be about 1200 visible SNRs in our sky but we’ve only managed to find about 300. That was until “Hoinga” was recently discovered. Named after the hometown of first author Scientist Werner Becker, whose research team found the SNR using the eROSITA All-Sky X-ray survey, Hoinga is one of the largest SNRs ever seen.

Hoinga is big. Really big. The SNR spans 4 degrees of the sky – eight times wider than the Full Moon. The obvious question – how could astronomers not have already found something THAT enormous? Hoinga is not where we typically are looking for supernova. Most of our SNR searches are focused on the plane of the Galaxy toward the Milky Way’s core where we’d expect to find the densest concentration of older and exploded stars. But Hoinga was found at high latitudes off the plane of the Galaxy.

Furthermore, Hoinga hides in the sky because it’s so large. At this scale, the SNR is difficult to distinguish from other large structures of dust and gas that make up the Galaxy known as the “Galactic Cirrus.” It’s like trying to see an individual cloud in an overcast sky. The Galactic Cirrus also outshines Hoinga in radio light, often used to search for SNRs, forcing Hoinga to hide in the background. Cross referencing with older radio sky surveys, the research team determined Hoinga had been observed before but was never identified as an SNR due to its comparatively faint glow in radio. Here eROSITA has an advantage as it sees X-rays. Hoinga shines brighter in X-ray light than the Galactic Cirrus allowing it to stand out from the Galaxy to be discovered.

Continue reading “An All-Sky X-Ray Survey Finds the Biggest Supernova Remnant Ever Seen” »

Many machine learning algorithms on quantum computers suffer from the dreaded “barren plateau” of unsolvability, where they run into dead ends on optimization problems. This challenge had been relatively unstudied—until now. Rigorous theoretical work has established theorems that guarantee whether a given machine learning algorithm will work as it scales up on larger computers.

“The work solves a key problem of useability for quantum machine learning. We rigorously proved the conditions under which certain architectures of variational quantum algorithms will or will not have barren plateaus as they are scaled up,” said Marco Cerezo, lead author on the paper published in Nature Communications today by a Los Alamos National Laboratory team. Cerezo is a post doc researching quantum information theory at Los Alamos. “With our theorems, you can guarantee that the architecture will be scalable to quantum computers with a large number of qubits.”

“Usually the approach has been to run an optimization and see if it works, and that was leading to fatigue among researchers in the field,” said Patrick Coles, a coauthor of the study. Establishing mathematical theorems and deriving first principles takes the guesswork out of developing algorithms.

Just because a mathematical formula works does not mean it reflects reality.