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TianGong Ultra Beat Usain Bolt’s Record, But Braking Remains Unsolved

TianGong Ultra Beat Usain Bolt’s 100m Record — Then Admitted It Can’t Brake Safely.

China’s TianGong Ultra ran 100 meters in 8.64 seconds at the World Humanoid Robot Games (Usain Bolt’s record: 9.58s).

Impressive speed. Real sim-to-real transfer under competition pressure.

But the robot’s own lead engineer said braking remains unsolved. At 75 kg and speeds over 17 m/s, the robots used crash mats because safe stopping wasn’t ready yet.

This is a genuine technical achievement. It is not yet commercial readiness.

For anyone evaluating humanoids for industrial use: competition records are exciting, but the ability to stop safely at those speeds is the real question that matters.

Full analysis:

Continue reading “TianGong Ultra Beat Usain Bolt’s Record, But Braking Remains Unsolved” | >

A wandering black hole caught feeding on the run

Astronomers have found the first direct evidence that a wandering black hole can feed itself by dragging gas along in its wake as it moves through its galaxy. It’s the first direct evidence of an accretion channel long predicted in theory but never before observed. The paper describing this discovery was posted to the arXiv preprint server on Aug. 11.

The black hole investigated in this study, led by Xin Li of Westlake University in China, is located in UGCA 320—an edge-on dwarf irregular galaxy about 20 million light-years away. A Hubble Space Telescope image showed that this object sat outside the galaxy’s main star-forming disk. MUSE observations from 2021 revealed broad Balmer emission, a signature of an accreting massive black hole. The broad Balmer emission-line component revealed the black hole’s mass to be around 35,000 times the sun’s mass. Multiple independent observations support its identification as an accreting intermediate-mass black hole.

The traits of this “wandering” black hole checked out. Intermediate-mass black holes have masses ranging from 100 to 100,000 times the sun’s mass. They are thought to be the seeds of the supermassive black holes found at galaxy centers. Some are expected to end up drifting far from the gas-rich centers that normally feed them.

Could quantum protocols make electronic voting more secure?

Electronic voting, the use of electronic systems to cast, record or count votes, could potentially simplify the process of electing new political leaders or other representatives. While some countries have already started using internet-connected devices or electronic voting machines at polling stations, the trustworthiness, security and anonymity of electronic voting systems are still widely debated.

Two distinct research teams, based at Sorbonne University and at the University of Geneva, recently carried out similar experiments assessing the potential of quantum physics-informed electronic voting protocols. The results of their tests, both published in Physical Review Letters, indicate that quantum protocols could allow votes to be collected electronically while protecting voter anonymity, without relying on a trusted central election authority.

Cacao genetics could help breeders reduce heavy metal levels in cocoa beans

Cadmium is a naturally occurring, toxic heavy metal found in many soils across Latin America and the Caribbean, where cacao trees are widely cultivated. It makes its way into cocoa seeds, called beans, that are roasted and made into chocolate, according to the Food and Agriculture Organization of the United Nations (FAO)—but some cacao varieties take up less of the toxic element than others. An international team of researchers, including Penn State scientists, found genetic differences in how two varieties managed cadmium uptake and facilitated the metal’s movement throughout the plant.

The findings—published this week (Sept. 8) in Plant and Soil—may be an early step toward breeding cacao that accumulates less cadmium, according to the collaborators from Penn State and Corporación Colombiana de Investigación Agropecuaria–AGROSAVIA (the Colombian Agricultural Research Corporation).

While trace amounts of cadmium in chocolate don’t present a serious risk to public health, long-term exposure to too much cadmium can lead to bone fragility as well as kidney and lung damage, explained one of the study’s two senior authors, Siela Maximova, research professor of plant biotechnology in Penn State’s College of Agricultural Sciences.

New Apple CEO unveils latest iPhone lineup, including a foldable model called Duo

Apple on Wednesday unveiled its latest generation of iPhones, including a widely anticipated foldable version called Duo.

The company’s new CEO, John Ternus, who took over from Tim Cook on Sept. 1, introduced the latest lineup at the Steve Jobs Theater.

After cycling through other updates to the iPhone, the Apple Watch and AirPods along with artificial intelligence advances, Ternus told the audience that “actually there is one more thing,” in a nod to the trademark phrase from Jobs that Jobs himself borrowed from the 1970s detective show “Columbo.”

3D magnetic-field control reveals new way to tune spin textures

(Fe0.63 Ni0.3 Pd0.07)3 P, or FNPP, is a magnetic material that exhibits complex magnetic structures even at room temperature. This makes the material of interest for spintronics, a field that could enable data processing with significantly lower energy consumption. One potential application is novel magnetic memory devices.

However, generating and modifying the desired structures in a controlled manner remains a challenge. A new study led by HZB has taken a step forward in this regard. The team demonstrated at the world’s only VEKMAG station at BESSY II that a tiny external B-field in the plane of the magnetic patterns is sufficient to change them. The work is published in the journal Advanced Functional Materials.

The team led by Dr. Florin Radu investigated FNPP samples using soft X-rays and ptychography at BESSY II. The experiments aimed to map the magnetic textures while the sample was exposed to an external magnetic field in specific spatial directions. For this purpose, BESSY II is equipped with a globally unique instrument developed by Radu’s team: the VEKMAG vector magnet can generate a magnetic field of up to 1 T in all three spatial directions.

Researchers chart new course for AI-powered biomedical discoveries

University of Missouri researchers are paving the way as artificial intelligence transforms biomedical research. A team from the College of Engineering and collaborators recently published one of the most comprehensive reviews to date of an emerging AI approach for biology known as flow matching. The work, published in Nature Machine Intelligence, provides scientists around the world with a roadmap for applying the technology to accelerate drug discovery, precision medicine and other biomedical advances.

“Flow matching helps computers learn how biology changes from one state to another,” said Jianlin “Jack” Cheng, a Curators’ Distinguished Professor and Paul K. and Diane Shumaker Professor in Bioinformatics. “This gives scientists a powerful new way to study everything from protein folding to cell development and cancer progression.”

Tiny 2D cracks creep through materials before triggering sudden fracture, experiments reveal

We have all seen something suddenly break: a phone screen cracks, a plastic object snaps or a piece of glass shatters. To our eyes, the failure seems to happen all at once. But what if the most important part of the break happens long before the final snap?

A new study presents a physical picture of how materials fail. The researchers found that cracks can begin as tiny, two-dimensional patches that grow extraordinarily slowly, at speeds ranging from microns to millimeters per second. Only after these patches grow to span the thickness of the material do they transform into the rapidly moving cracks associated with sudden, explosive fracture.

The research, published in the journal Physical Review Letters, was conducted by Yuval Paz and Jay Fineberg of the Racah Institute of Physics at The Hebrew University of Jerusalem, together with Meng Wang of the Beijing Institute of Technology and Mokhtar Adda-Bedia of CNRS, ENS de Lyon and Université de Lyon.

Unconventional quantum materials could dramatically boost the search for dark matter

For decades, physicists have searched for dark matter, the invisible substance thought to make up roughly 85% of all matter in the universe. Although its gravitational influence shapes galaxies and the large-scale structure of the cosmos, dark matter has never been directly detected. Now, an international team has identified a new class of quantum materials that could dramatically improve the search for some of the lightest and most elusive forms of dark matter.

Published in Physical Review Letters, the study introduces three unconventional materials whose unique electronic properties could serve as exceptionally sensitive dark matter detectors, potentially surpassing existing detector designs by several orders of magnitude.

The research was conducted by Prof. Yonit Hochberg and Rotem Ovadia from the Hebrew University of Jerusalem, Dr. Dino Novko from the Institute of Physics in Croatia, and Prof. Antonio Politano of the University of L’Aquila. The work brings together expertise in particle physics, condensed matter physics and materials science to tackle one of the greatest unanswered questions in modern science.

Physics-aware benchmark reveals why similar materials AI models can predict thermal conductivity differently

Material properties such as sound insulation, resistance to extreme heat and thermal expansion originate from how the zillions of microscopic building blocks (nuclei and electrons) interact at equilibrium and respond to perturbations. Atoms are typically about one ten-billionth of a meter across, so there can be a lot of parts to keep track of—a task that is complicated at the quantum-mechanical level, where particles are neither here nor there until observed.

In recent years, researchers have turned to machine learning (ML) to overcome the challenges of tracking countless quantum particles while connecting these atomic-level details to observable physical properties. Models abound, but can they be trusted?

In a new paper published in Nature Communications, Michele Simoncelli, assistant professor of applied physics at Columbia, sets a benchmark for evaluating ML models that aim to predict the thermal and mechanical properties of different materials.

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