alternative_right shares a report from Phys.org: Scientists from the Czech Republic have created microscopic cleanup robots that successfully removed microplastics from both soil and aquatic environments in the lab. Details of their work are published in the journal npg Asia Materials. To build the robots, researchers started with microscopic MXene particles made of ultrathin, stacked layers. The material has a large surface area and surface chemistry that help it attract and hold plastic. They then coated the particles with magnetic nickel nanoparticles so rotating magnetic fields could drive the tiny robots.

Then came the testing phase. The team placed swarms of these microbots into water and soil samples contaminated with plastic. The externally generated magnetic fields made the robots spin and tumble through the liquid and squeeze through tiny water-filled gaps in the soil. "In soil environments, the microrobots can navigate through water-permeated soil microenvironments, actively disrupt microplastic entrapment within soil matrices, and extract them," the study authors commented in their paper. As they moved, their sticky surfaces collided with microplastics, binding to them and dragging them along. The researchers then used a magnet to pull the microrobots, along with their trapped plastic cargo, out of the samples.

They performed well. In water, they removed about 94% of test polystyrene particles and 89% of PET (polyethylene terephthalate) in around an hour. In the model soil, they removed about 81% of the polystyrene and 72% of PET. This outperformed the same MXene material used without magnetic motion, which relied on passive adsorption rather than actively moving through soil and water.

Ian Clarke (aka ancient Slashdot reader Sanity), designer of the peer-to-peer communication platform Freenet, is back with an update on the project's progress following the launch of its P2P network in March. He writes: Earlier this year, Slashdot covered the launch of the completely redesigned Freenet. I recently gave a talk about what we've been building since then. Unlike traditional web applications, apps on Freenet have no central server or database; instead application state is distributed across the network. These now include decentralized group chat, publishing, search, and fully decentralized Git hosting. The talk also gets into some of Freenet's internals, including how we use machine learning for network routing.

Starting today, unsolicited telemarking calls are banned in France unless consumers explicitly consent to them. Companies that violate the law can face fines of up to 375,000 euros per call, though existing customers can still be contacted with related offers. Le Monde reports: The government says the law is a response to years of consumer complaints. Authorities estimate that about three-quarters of people in France receive at least one unsolicited sales call every week, and many receive more. In 2024, 11 consumer organizations issued a joint call for a ban, denouncing "relentless harassment of consumers through countless unwanted telemarketing calls to both landlines and mobile phones -- an intrusion that has become a regular part of their daily lives."

Now, "businesses are prohibited from contacting consumers without their prior consent," said Alice Vilcot, chief of staff at the Directorate-General for Competition, Consumer Affairs and Prevention of Fraud. "That consent can be withdrawn at any time." [...] Previously in France, people who wanted to avoid marketing calls had to register their number with a government-run service, but consumer groups said some call centers ignored the list. Vilcot noted that an Ireland-based company was fined 6 million euros ($6.9 million) last year for violating France's previous telemarketing rules by calling people on the no-call list.

Longtime Slashdot reader DF5JT writes: On July 26, I posted a single announcement in r/backgammon: GNU Backgammon for Android, GPLv3, the first standalone backgammon engine on F-Droid. Reddit's spam filter removed the post and permanently banned my 11-year, 30,000-karma account -- the result is publicly visible at reddit.com/user/OE1FEU. To this day, Reddit has given no reason whatsoever, although Article 17 of the EU's Digital Services Act makes a statement of reasons mandatory. The only appeal channel is a 250-character web form that sends no confirmation and has never been answered; Reddit's own help text admits: "you may not have received a message to your inbox." A GDPR export of 11 years of data came to 7.2 MB; every post was truncated after a few lines, with zero data about the ban decision.

So I spent one day escalating through every mechanism the EU provides: certified out-of-court dispute settlement at Austria's RTR, complaints with the Austrian and Dutch Digital Services Coordinators, the data protection authority (citing the ECJ's SCHUFA ruling on automated decisions), noyb, and Austria's consumer association. Bonus finding: the European Commission's DSA Transparency Database contains 14,067 Reddit statements of reasons for that week -- none for my ban -- and the Commission's own feedback form limits reports to 500 characters and crashed with a 500 Server Error. Is the DSA enforceable for ordinary users, or just paperwork?

Researchers believe they've found evidence that brain signals reveal the pitches of notes in imagined musical melodies, reports Medical Express: Study participants were 10 epilepsy patients with electrodes on the surfaces of their brains for clinical monitoring. Participants quietly listened to the first bars of children's tunes, then imagined the melodies of subsequent bars before humming their imagined melodies. Brain activity from the patients carried information about relative pitches within the imagined melodies. Combining note-level pitch predictions over time allowed the research team to recreate melodic changes that reflected the structure of the imagined songs...

"The model we created decodes relative pitch classes — such as do, re, mi, fa, sol, and la — rather than exact, absolute pitches," [said tudy co-lead Jii Kwon of Seoul National University]. "This is useful because people often recognize melodies by the relationships between notes, even when the same melody is played in a different key."

Researchers in China have developed a living mycelium textile that can self-clean, renew its surface, and partially repair holes when treated with a nutrient solution and fresh fungus. The material can also gain added properties such as blue pigmentation or UV resistance by co-culturing it with yeast or other fungi. Dezeen reports: The breakthrough from the researchers at the Shenzhen Institutes of Advanced Technology is a type of engineered living material (ELM) -- a material built off living organisms that stay active even after they're fabricated into their final form. [...] By working with living but dormant cordyceps militaris fungus instead, the researchers have been able to take advantage of its biological functions. The result is a material that is self-renewing and responsive to its environment, in ways that could one day transform architecture and clothing -- as seen in a prototype dress created together with material innovation company Peelshere.

It can also be adapted by mixing in other fungi or yeast, lead researcher Ke Li and her team detail in a paper in the peer-reviewed journal Science Advances. In it, they describe a "programmable fungal platform" where mycelium is treated like a modular system, with the sheet material forming a base structure and extra biological abilities, such as colour and UV resistance, becoming "plug-and-play" add-ons via other organisms. This gets their textile closer to the self-repair, environmental responsiveness and controllable functionality that is the promise of engineered living materials, they argue.

The ELM's self-renewing and semi-repairing functionality comes from the mycelium base structure. Following drying at 45 degrees, the material is not quite living and not quite dead, but instead in a "low-metabolic, dormant-like state", Li told Dezeen, meaning it is not actively growing. However, new growth can be triggered by applying a nutrient solution of potato water, leading the dormant mycelium to germinate, send out new fungal filaments and renew the material's surface. When this nutrient solution is applied over a hole, along with a small patch of fresh fungus, it triggers the living cells to grow across the gap, seamlessly repairing the surface without any adhesives or stitching. The material is also naturally self-cleaning, as it is hydrophobic. "Its distinctive surface texture, biological colouring, controlled repair and biodegradability may be particularly useful in applications where visual expression and a defined product lifetime are important," said Li.

"Further improvements in durability, moisture resistance, safety and manufacturing consistency would be needed before it could be considered for routine clothing or permanent architectural use."

Longtime Slashdot reader mccalli writes: Times Magazine has started serving adverts with "brand messages" to AI crawlers, which are not part of its main page. The aim seems to be to influence chatbots to talk about the brand offerings in a positive light that can't be traced back to the actual page. Notably, they've excluded Google chatbots from this, likely because Google penalizes companies that show different search offerings to its bots versus actual human visitors. "Maybe it's more important to influence the agent than even the human, because with a human you influence one person. When you influence ChatGPT, you're influencing potentially all of ChatGPT," said Jonah Goodhart, co-founder and CEO of ad tech platform Mobian. "If ChatGPT changes what it says about [a brand], it's massive and it's more than any one campaign could ever do. The big idea here is LLMs and AI want trusted information about brands. We're serving that up to them in a very straightforward, easy way."

The ad strategy was uncovered by Germany-based freelance software developer Vincent Schmalbach, who detailed his findings in a blog post.

Digiday and The Register both reported on the story.

The FDA has approved the first mRNA flu vaccine in the United States after a clinical trial found it was about 27% more effective than a standard flu shot. Manufactured by Moderna and marketed as mFlusiva, the vaccine is expected to be available this fall for adults ages 50 to 64 and those 65 and older, though approval for the older group is conditional on Moderna conducting an additional clinical trial, NBC News reports. From the report: Many scientists and public health experts have touted the idea of an mRNA-based flu vaccine, which uses the same messenger RNA platform as the Covid vaccines from Moderna and Pfizer. That's because mRNA vaccines can be manufactured much faster than traditional vaccines, allowing scientists to better match circulating influenza strains. Moderna said it takes two to three months from picking the strain to rolling out its flu shot, compared with about six months for traditional flu shots.

Longtime Slashdot reader AmiMoJo shares a report from Electrek: Waymo co-CEO Dmitri Dolgov laid out the clearest technical case yet for why cameras alone can't take a self-driving system to full autonomy, arguing that "weak sensing" hits a safety ceiling long before it reaches superhuman performance. [...] Dolgov made the comments in a talk at Y Combinator's Startup School, walking through the lessons Waymo has learned building its driver over close to two decades. He put the sensor question on the table plainly: "there's been a long-standing debate about what kind of sensors do you actually need for autonomous driving."

His answer draws the line that camera-only advocates tend to skip right past. "Humans of course can drive with just eyes, so there's that proof of existence," he said. "If the goal were to just approximately match human performance or to build an assist product, that's a very reasonable way to go." Then the catch. If you're targeting full autonomy and strongly superhuman performance, he said, "you find that weak sensing just leads to a safety curve that flattens out way too early." Dolgov said that cameras, lidar, and radar are complementary rather than redundant: "These different sensing modalities, they're not backups to each other," and combining them produces a view "vastly superior to what you get with any one sensor." He said multiple sensor types also protect against physical failures, such as a leaf or branch blocking a camera, while helping Waymo climb the "exponential ladder of nines" required for fully driverless safety.

Dolgov warned that camera-only systems may improve quickly before plateauing "way before the performance that is required by your product." He also pushed back on the cost argument against lidar, calling it "a number that has a fairly short shelf life" as hardware costs continue to fall.

To design invisible drones, researchers have tried camouflage, transparent materials and light-bending optical systems. But engineers at Northwestern University used "motion blur," which an announcement from the school notes is the effect that makes fast-spinning fans seem to disappear.

"The drone spins up to 25 times per second, which is too fast for the human eye to see clearly. While it isn't completely invisible, it morphs into a ghostly smudge that seamlessly blends into the background... " To design the drone, the Northwestern team first used a computational model to generate roughly 20,000 drone configurations capable of stable flight. Then, they used artificial intelligence (AI) and optimization algorithms to repeatedly rearrange the drones' major components, including a motor, propeller, circuit board, counterweight and batteries. After sifting through many different configurations, the algorithms determined the ideal placement of the drone's components to minimize its visibility from virtually every viewing angle while allowing for stable flight.

After selecting promising candidates, the engineers simulated each drone spinning in flight and overlaid those images a hundred real-world backgrounds. Then, they used a perception model that approximates human vision to determine how noticeable each design appeared. Designs that blended into their surroundings received lower visibility scores. The team selected the 500 lowest-scoring designs and applied the optimization algorithm, which repeatedly adjusted the positions of components to further minimize those scores. "The design process was fully automated," [said Northwestern's associate CS professor Michael Rubenstein, who led the work]. "Then, when we were confident that a drone met all our criteria, we built it."
They've named the drone "Phantom Twist"," and plan to design future iterations with more transparent materials or quieter propulsion to make it even less noticeable.

The Northwestern team presented the work on July 16 at Robotics: Science and Systems 2026 in Sydney, Australia...

Thanks to long-time Slashdot reader fahrbot-bot for sharing the news.