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One of the astronauts stuck in space after Starliner malfunction to be on Cape Cod Feb. 20

She is an inspiration!

NASA astronaut Sunita “Suni” Williams, a Needham native with Falmouth ties, will speak about her experiences during a recent space mission at 7:30 p.m. Feb. 20 at the Marine Biological Laboratory’s Falmouth Forum, according to a community announcement.

The lecture, titled “So Much Space… So Much Time!,” will take place in the Cornelia Clapp Auditorium in Lillie Laboratory, 7 MBL St., Woods Hole. It is free and open to the public.

Williams and fellow astronaut Butch Wilmore remained aboard the International Space Station after thruster failures on their spacecraft. They returned to Earth on an alternate vehicle. Williams will share videos and personal accounts to highlight the rapid commercialization of space and the challenges it presents.

Quantum States Stay Frozen in First Experimental Test of Statistical Localization

PRESS RELEASE — In the everyday world, governed by classical physics, the concept of equilibrium reigns. If you put a drop of ink into water, it will eventually evenly mix. If you put a glass of ice water on the kitchen table, it will eventually melt and become room temperature.

That concept rooted in energy transport is known as thermalization, and it is easy to comprehend because we see it happen every day. But this is not always how things behave at the smallest scales of the universe.

In the quantum realm—at the atomic and sub-atomic scales—there can be a phenomenon called localization, in which equilibrium spreading does not occur, even with nothing obviously preventing it. Researchers at Duke University have observed this intriguing behavior using a quantum simulator for the first time. Also known as statistical localization, the research could help probe questions about unusual material properties or quantum memory.

New polymer alloy could solve energy storage challenge

In the race for lighter, safer and more efficient electronics—from electric vehicles to transcontinental energy grids—one component literally holds the power: the polymer capacitor. Seen in such applications as medical defibrillators, polymer capacitors are responsible for quick bursts of energy and stabilizing power rather than holding large amounts of energy, as opposed to the slower, steadier energy of a battery.

However, current state-of-the-art polymer capacitors cannot survive beyond 212 degrees Fahrenheit (F), which the air around a typical car engine can hit during summer months and an overworked data center can surpass on any given day.

In Nature, a team led by Penn State researchers reported a novel material made of cheap, commercially available plastics that can handle four times the energy of a typical capacitor at temperatures up to 482 F.

Machine learning algorithm fully reconstructs LHC particle collisions

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly and precisely than traditional methods, helping physicists better understand LHC data. The paper has been submitted to the European Physical Journal C and is currently available on the arXiv preprint server.

Each proton–proton collision at the LHC sprays out a complex pattern of particles that must be carefully reconstructed to allow physicists to study what really happened. For more than a decade, CMS has used a particle-flow (PF) algorithm, which combines information from the experiment’s different detectors, to identify each particle produced in a collision. Although this method works remarkably well, it relies on a long chain of hand-crafted rules designed by physicists.

The new CMS machine-learning-based particle-flow (MLPF) algorithm approaches the task fundamentally differently, replacing much of the rigid hand-crafted logic with a single model trained directly on simulated collisions. Instead of being told how to reconstruct particles, the algorithm learns how particles look in the detectors, like how humans learn to recognize faces without memorizing explicit rules.

Tower Semiconductor and Scintil Photonics Announce Availability of World’s First Heterogeneously Integrated DWDM Lasers for AI Infrastructure

Combined with Tower’s multi-site global footprint, Scintil’s unique SHIP™ platform is ready to take on the challenging requirements of the next generation Hyperscale AI Infrastructure Scintil Photonics LEAF Light™ Scintil Photonics’ LEAF Light™ is the industry’s first single-chip DWDM-native light engine, delivering high-density, low-power optical connectivity for next-generation AI factories. MIGDAL HAEMEK, Israel and GRENOBLE, France, Feb. 17, 2026 (GLOBE NEWSWIRE) — Tower Semiconductor (NASD

Scientists May Let You Regrow Teeth by 2030

Sink your teeth into this.

Japanese scientists are advancing human clinical trials of a drug that could allow people to regrow lost teeth.

The treatment targets a gene called USAG1, which normally shuts down tooth development after your adult teeth come in. By blocking that gene, researchers are essentially restarting the body’s natural tooth-growth process.

Phase I trials began in September 2024 with 30 adult men missing at least one tooth. If successful, the treatment could potentially be available by around 2030.

Dentures? Implants?
What if you could just… grow a new tooth?

Would you try this?

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