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A 4km Drive That Changed Physics: The First Antimatter Transport

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Hello and welcome! My name is Anton and in this video, we will talk about the first ever antimatter transportation using a truck
Links:
https://www.nature.com/articles/s4158… #science #cern.

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US reportedly charges Scattered Spider hacker arrested in Finland

A 19-year-old dual United States and Estonian citizen arrested in Finland earlier this month faces federal charges in the U.S. alleging he was a prolific member of the notorious Scattered Spider hacking collective.

According to temporarily unsealed court records obtained by the Chicago Tribune, the suspect (who used the online alias “Bouquet”) helped extort millions of dollars from multiple large corporations worldwide.

The suspected Scattered Spider member, who was allegedly arrested by Finnish law enforcement at Helsinki’s airport on April 10 while attempting to board a flight to Japan, is facing wire fraud, conspiracy, and computer intrusion charges.

Solar reactor uses old battery acid to turn plastic waste into clean hydrogen

Researchers have developed a solar-powered reactor to break down hard-to-recycle forms of plastic waste—such as drink bottles, nylon textiles and polyurethane foams—using acid recovered from old car batteries, and converting it into clean hydrogen fuel and valuable industrial chemicals. The results are reported in the journal Joule.

The reactor, developed by researchers from the University of Cambridge, is powered by the energy from the sun, and could be a cheaper, more sustainable alternative to current chemical-based recycling methods. The team says their method could create a circular system where one waste stream solves another.

Global plastic production is more than 400 million tons per year, yet only 18% is recycled. The rest is burned, landfilled, or leaks into ecosystems. The researchers believe that their method, known as solar-powered acid photoreforming, could become part of the solution to the global mountain of plastic waste.

AI Models Mirror Human Logic on Real-World Scenarios

“What we show is that the models actually capture that human uncertainty pretty well,” said Michael Lepori. [ https://www.labroots.com/trending/technology/30475/ai-models…cenarios-2](https://www.labroots.com/trending/technology/30475/ai-models…cenarios-2)

Can AI models distinguish fact from fiction? This is what a new study scheduled to be presented at the International Conference on Learning Representations this weekend hopes to address as a team of scientists investigated how AI models could tell the difference between facts and “fake news”. This study has the potential to help scientists, engineers, and the public better understand how AI models can evolve to meet human needs, which comes at a time when AI is becoming more integrated into our everyday lives.

For the study, the researchers analyzed how AI language models (LMs) were able to differentiate between different topics and information and judge what’s true and what’s fake. The motivation behind this study was to address a knowledge gap regarding whether large language models (LLMs) have a human-like understanding of the world or if they simply make decisions based on what’s given to them.

The goal of the study was to ascertain if the LMs could determine whether an event is real or fake, along with ascertaining when the LM makes this determination during its thought process. For example, the researchers would give the LM simple scenarios like “clean a car”, clean a road”, and “clean a cloud”, and ask the LM to figure out which was real or fake. In the end, the researchers found that large LMs were capable of differentiating between real and fake events or data.

It wasn’t just water: The hidden force inside Japan’s 2011 tsunami changed everything

Mud-rich coastlines could face a greater tsunami risk, at least that may have been the case for the 2011 Tōhoku-oki tsunami that killed more than 19,000 people and led to the Fukushima Daiichi nuclear disaster. According to a new study published in the Journal of the Geological Society, mud may have made the catastrophic ocean waves more destructive than they might otherwise have been.

On 11 March 2011, a powerful earthquake off the coast of Honshu, Japan’s main island, triggered a massive tsunami. A wall of water swept away boats, cars, and buildings as it surged inland.

Patrick Sharrocks from the University of Leeds and colleagues studied helicopter news footage of the event, noting how the wave passed specific landmarks, such as greenhouses, houses, and road signs, to calculate its speed. They also compared before and after images from Google Earth to measure distances between landmarks and calculate how steep the front of the wave was.

Excuse me, is that solar panel pointing in the right direction?

On a bright morning, graduate student Jeremy Klotz and professor Shree Nayar walked through upper Manhattan with a tall tripod and a camera that takes 360-degree images. Their route took them to bike docking stations, which use solar energy to power their kiosks, docking mechanisms, wireless communication, and even E-bike recharging in recent installations. At each docking station, the researchers raised the camera above the panel, snapped a spherical picture, and sent it to Klotz’s laptop.

Seconds later, the team’s computer vision program told them something remarkable: how much energy that panel would generate in a year—and how much it could generate if it were pointed at the optimal angle.

As it turns out, the solar panels powering the bike docking stations—and likely many solar panels across New York City and other urban destinations—may be leaving significant energy untapped simply because they are not at their best orientation.

How maze-like magnetic patterns form and evolve in materials

The rapid increase in electric vehicle adoption in recent years has highlighted a crucial issue: the energy conversion efficiency of electric motors. In electric motors, iron loss or magnetic hysteresis loss is a primary source of energy dissipation, arising from the repeated reversal of magnetic fields within the motor core, made of soft magnetic materials. Moreover, electric motors typically operate in high-temperature environments, where thermal effects can lead to partial demagnetization, further complicating energy-loss mechanisms.

The structure of magnetic domains (tiny magnetic regions) of soft magnetic materials strongly influences their magnetic properties, including response to high temperatures and hysteresis loss.

Magnetic domains exhibit a variety of fine structures. In some soft magnetic materials, they form intricate zig-zag patterns known as maze domains. These maze domains show complex and abrupt temperature-dependent behavior that can significantly affect energy loss.

Transparent cooling film cuts car cabin temperature by 6.1°C without electricity

A transparent radiative cooling film technology that dissipates heat directly to the outside without consuming electricity has been developed to reduce vehicle overheating during summer. The technology was validated through real-vehicle experiments conducted under diverse conditions—including different countries, seasons, and both parking and driving scenarios—and demonstrated the ability to lower cabin temperatures by up to 6.1°C and reduce cooling energy consumption by more than 20%.

Seoul National University College of Engineering announced that a research team led by Prof. Seung Hwan Ko (Department of Mechanical Engineering, SNU), in collaboration with Prof. Gang Chen at MIT and research teams from Hyundai Motor Company and Kia (Materials Research & Engineering Center and Thermal Energy Total Development Group), has designed and fabricated a large-area Scalable Transparent Radiative Cooling (STRC) film applicable to vehicle windows. Through real-vehicle evaluations conducted under various climatic and driving conditions, the team demonstrated both energy-saving and carbon reduction effects.

This research was published online on February 4 in the journal Energy & Environmental Science.

Frozen. Thawed. Not dead: Jean Hilliard’s amazing Minnesota story

When you Google the small town of Lengby, there’s pretty much just one result that pops up, something that happened almost 40 years ago. The accounts online call it a miracle.

On the night of Dec. 20, 1980, 19-year-old Jean Hilliard’s car hit the ditch. She tried to walk for help. She was found in the morning in the front yard of a local cattle rancher — frozen solid as a log.

Machine learning accelerates analysis of fusion materials

Tungsten’s superior performance in extreme environments makes it a leading candidate for plasma-facing components (PFCs) in fusion reactors, but the ultra-high heat can damage its microscopic structure and lead to component failure. Scanning electron microscopy (SEM) can capture and quantify these microstructure changes, but assembling a sufficiently large dataset of SEM imagery is expensive and logistically challenging.

To augment this dataset, researchers at Oak Ridge National Laboratory trained a generative machine learning model using 3,200 SEM images of tungsten samples exposed to fusion-relevant conditions. The model can generate novel SEM images with realistic microstructures and surface features, such as cracks and pores, without replicating the original images.

“This work is not about making pretty pictures, it’s about capturing the statistics of real damage on these materials,” said ORNL’s Rinkle Juneja, the project’s principal investigator. “We train our generative workflow to learn tungsten’s microstructure signatures, like crack patterns, so it can generate new, statistically consistent microstructures, laying the groundwork for robust, data-driven assessment of PFC fusion materials.”

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