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Partial differential equations (PDEs) are a class of mathematical problems that represent the interplay of multiple variables, and therefore have predictive power when it comes to complex physical systems. Solving these equations is a perpetual challenge, however, and current computational techniques for doing so are time-consuming and expensive.

Now, research from the University of Utah’s John and Marcia Price College of Engineering is showing a way to speed up this process: encoding those equations in light and feeding them into their newly designed “optical neural engine,” or ONE.

The researchers’ ONE combines diffractive optical neural networks and optical matrix multipliers. Rather than representing PDEs digitally, the researchers represented them optically, with variables represented by the various properties of a light wave, such as its intensity and phase. As a wave passes through the ONE’s series of optical components, those properties gradually shift and change, until they ultimately represent the solution to the given PDE.

For the first time, scientists have used Earth-based telescopes to look back over 13 billion years to see how the first stars in the universe affect light emitted from the Big Bang.

Using telescopes high in the Andes mountains of northern Chile, astrophysicists have measured this polarized microwave light to create a clearer picture of one of the least understood epochs in the history of the universe, the Cosmic Dawn.

“People thought this couldn’t be done from the ground. Astronomy is a technology-limited field, and from the Cosmic Dawn are famously difficult to measure,” said Tobias Marriage, project leader and a Johns Hopkins professor of physics and astronomy. “Ground-based observations face additional challenges compared to space. Overcoming those obstacles makes this measurement a significant achievement.”

Astronomers have discovered the largest known cloud of energetic particles surrounding a galaxy cluster—spanning nearly 20 million light-years. The finding challenges long-standing theories about how particles stay energized over time. Instead of being powered by nearby galaxies, this vast region seems to be energized by giant shockwaves and turbulence moving through the hot gas between galaxies.

Coordinated behaviors like swarming—from ant colonies to schools of fish—are found everywhere in nature. Researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have given a nod to nature with a next-generation robot system that’s capable of movement, exploration, transport and cooperation.

A study in Science Advances describing the new soft robotic system was co-led by L. Mahadevan, the Lola England de Valpine Professor of Applied Mathematics, Physics, and Organismic and Evolutionary Biology in SEAS and the Faculty of Arts and Sciences, in collaboration with Professor Ho-Young Kim at Seoul National University. Their work paves new directions for future, low-power swarm robotics.

The new robots, called link-bots, are comprised of centimeter-scale, 3D-printed particles strung into V-shaped chains via notched links and are capable of coordinated, life-like movements without any embedded power or control systems. Each particle’s legs are tilted to allow the bot to self-propel when placed on a uniformly vibrating surface.

Researchers at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have made a breakthrough in laser technology by using machine learning (ML) to help stabilize a high-power laser.

This advancement, spearheaded by Berkeley Lab’s Accelerator Technology & Applied Physics (ATAP) and Engineering Divisions, promises to accelerate progress in physics, medicine, and energy. The researchers report their work in the journal High Power Laser Science and Engineering.

A research team from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has developed a compact dynamic cantilever magnetometer with a diameter of just 22 mm, achieving magnetic moment sensitivity on the order of 10-17 A·m2.

“This breakthrough fills a technological gap in ultra-sensitive magnetic measurements for small, low-dimensional materials under ,” said Prof. Wang Ning, a member of the team.

The study was published in Review of Scientific Instruments.

Technology veteran IBM on Tuesday laid out a plan to have a “practical” quantum computer tackling big problems before the end of this decade.

Current quantum computers are still experimental and face significant challenges, including high error rates. Companies like IBM, Google, and others are working to build more stable and scalable quantum systems.

Real-world innovations that quantum computing has the potential to tackle include developing better fuels, materials, pharmaceuticals, or even new elements. However, delivering on that promise has always seemed some way off.

The organization of this paper is as follows. In Section 2 we introduce the formalism used to estimate the number of lunar craters containing ore-bearing asteroid remnants. In Section 2.1, we use the formalism to estimate the number of PGM ore-bearing craters, and in Section 2.2, we use it to obtain the number of water-bearing craters, before concluding in Section 3.

If we chose to shovel matter along a straight line through the observable Universe, what is the average mass of matter per unit area that our shovel will collect? To answer this question, I did a simple calculation before my morning jog at sunrise today.

The answer depends on how far the shovel goes. Projecting all the matter out to the farthest galaxy, MoM-z14, discovered last month by the Webb telescope at a cosmic redshift 14.44 or equivalently 280 million years after the Big Bang, the answer is about 0.5 grams per square centimeter, of order the mass per unit area of a thumb. This establishes the cosmic rule-of-thumb: the observable universe yields on average as much mass per unit area as a thumb.

This mass budget includes mostly dark matter whose nature is unknown. Ordinary matter accounts for only 16% of the total budget or 0.08 grams per square centimeter out to MoM-z14, of order the surface mass density of a fingernail.