How can future lunar astronauts have power on a future lunar settlement without needing to have it delivered from Earth? This is what a recent study publis | Space
ABSTRACT. Seismic waves carry rich information about earthquake sources and the Earth’s medium. However, the process of extracting earthquake source parameters from seismic waves using traditional methods is complex and time consuming. In this study, we present a deep‐learning‐based method for automatic determination of earthquake source parameters. Considering the principle of calculating source parameters, the input of the deep neural network (SourceNet) includes not only the seismic waveform, but also the amplitude, epicenter distance, and station information. The utilization of multimodal data significantly improves the accuracy of determining earthquake source parameters. The test results using the real seismic data in the Sichuan–Yunnan region show that the earthquake source parameters obtained by SourceNet are in good agreement with the manual results and have higher computational efficiency. We apply the trained SourceNet to the seismic activities in the Changning area and further verify the reliability of the method by comparing our estimates of stress drops with those reported in previous studies of this area. The average time for SourceNet to calculate the source parameters of an earthquake is less than 0.1 s, which can be used for real‐time automatic determination of source parameters.
A new suggestion that complexity increases over time, not just in living organisms but in the nonliving world, promises to rewrite notions of time and evolution.
Surprising results from hypersonic air flow simulations could help design stronger, faster and more durable supersonic vehicles.
A team of engineers at Fudan University has successfully designed, built and run a 32-bit RISC-V microprocessor that uses molybdenum disulfide instead of silicon as its semiconductor component. Their paper is published in the journal Nature.
Most microprocessors are made using the semiconductor silicon, which has worked out well for several decades. But as researchers attempt to make processors ever smaller, they have run into a dead end with silicon—they cannot make it any thinner. Instead, many researchers have turned to 2D materials such as graphene, but this is challenging because it is a conductor, not a semiconductor.
In this new study, the research team used a nearly 2D semiconducting material, single-molecule sheets of molybdenum disulfide. These sheets are not truly 2D because they bond at an angle, resulting in a slightly zigzag surface. To make a processor out of them, they put them on a sapphire substrate.
The same dirt that clings to astronauts’ boots may one day keep their lights on. In a study published in Device, researchers created solar cells made out of simulated moon dust. The cells convert sunlight into energy efficiently, withstand radiation damage, and mitigate the need for transporting heavy materials into space, offering a potential solution to one of space exploration’s biggest challenges: reliable energy sources.
“The solar cells used in space now are amazing, reaching efficiencies of 30% to even 40%, but that efficiency comes with a price,” says lead researcher Felix Lang of the University of Potsdam, Germany. “They are very expensive and are relatively heavy because they use glass or thick foil as cover. It’s hard to justify lifting all these cells into space.”
Instead of hauling solar cells from Earth, Lang’s team is looking at materials available on the moon itself. They aim to replace Earth-made glass with glass crafted from lunar regolith —the moon’s loose, rocky surface debris. This change alone could cut a spacecraft’s launch mass by 99.4%, slash 99% of transport costs, and make long-term lunar settlements more feasible.
The world of robotics is undergoing a significant transformation, driven by rapid advancements in physical AI. This evolution is accelerating the time to market for new robotic solutions, enhancing confidence in their safety capabilities, and contributing to the powering of physical AI in factories and warehouses.
Announced at GTC, Newton is an open-source, extensible physics engine developed by NVIDIA, Google DeepMind, and Disney Research to advance robot learning and development.
NVIDIA Cosmos launched as a world foundation model (WFM) platform under an open model license to accelerate physical AI development of autonomous machines such as autonomous vehicles and robots.
Chromatin remodeling plays a vital role in gene regulation, affecting how DNA is accessed. Disruptions in this process can also lead to cancer and other diseases.
To better understand how chromatin remodeling works, scientists at St. Jude Children’s Research Hospital used cryo–electron microscopy (cryo-EM) to obtain fine structural details of a human chromatin remodeler in action.
The researchers captured 13 structures that together offer a comprehensive view of how the remodeling enzyme SNF2H works, offering insights that are likely shared across other such enzymes. The work was published today in Cell Research.
Unitree’s G1 demonstrates a new level of robotic agility with a complex movement following an AI software update.
Polymer-coated nanoparticles loaded with therapeutic drugs show significant promise for cancer treatment, including ovarian cancer. These particles can be targeted directly to tumors, where they release their payload while avoiding many of the side effects of traditional chemotherapy.
Over the past decade, MIT Institute Professor Paula Hammond and her students have created a variety of these particles using a technique known as layer-by-layer assembly. They’ve shown that the particles can effectively combat cancer in mouse studies.
To help move these nanoparticles closer to human use, the researchers have now come up with a manufacturing technique that allows them to generate larger quantities of the particles, in a fraction of the time.