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Honest or deceptive? What a new signaling model means for animal displays and human claims

For decades, scientists have tried to answer a simple question: why be honest when deception is possible? Whether it is a peacock’s tail, a stag’s roar, or a human’s résumé, signals are means to influence others by transmitting information and advantages can be gained by cheating, for example by exaggeration. But if lying pays, why does communication not collapse?

The dominant theory for honest signals has long been the handicap principle, which claims that signals are honest because they are costly to produce. It argues that a peacock’s tail, for example, is an honest signal of a male’s condition or quality to potential mates because it is so costly to produce. Only high-quality birds could afford such a handicap, wasting resources growing it, demonstrating their superb quality to females, whereas poor quality males cannot afford such ornaments.

A new synthesis by Szabolcs Számadó, Dustin J. Penn and István Zachar (from the Budapest University of Technology and Economics, University of Veterinary Medicine Vienna and HUN-REN Centre for Ecological Research, respectively) challenges that logic. They argue that honesty does not depend on how costly or wasteful a signal is, but rather on the trade-offs between investments and benefits, faced by signalers.

New design tool 3D-prints woven metamaterials that stretch and fail predictably

Metamaterials—materials whose properties are primarily dictated by their internal microstructure, and not their chemical makeup—have been redefining the engineering materials space for the last decade. To date, however, most metamaterials have been lightweight options designed for stiffness and strength.

New research from the MIT Department of Mechanical Engineering introduces a computational design framework to support the creation of a new class of soft, compliant, and deformable metamaterials. These metamaterials, termed 3D woven metamaterials, consist of building blocks that are composed of intertwined fibers that self-contact and entangle to endow the material with unique properties.

‘Discovery learning’ AI tool predicts battery cycle life with just a few days’ data

An agentic AI tool for battery researchers harnesses data from previous battery designs to predict the cycle life of new battery concepts. With information from just 50 cycles, the tool—developed at University of Michigan Engineering—can predict how many charge-discharge cycles the battery can undergo before its capacity drops below 90% of its design capacity.

This could save months to years of testing, depending on the conditions of cycling experiments, as well as substantial electrical power during battery prototyping and testing. The team estimates that the cycle lives of new battery designs could be predicted with just 5% of the energy and 2% of the time required by conventional testing.

“When we learn from the historical battery designs, we leverage physics-based features to construct a generalizable mapping between early-stage tests and cycle life,” said Ziyou Song, U-M assistant professor of electrical and computer engineering and corresponding author of the study in Nature. “We can minimize experimental efforts and achieve accurate prediction performance for new battery designs.”

Quick test can curb antimicrobial resistance, identifying bacteria and antibiotic susceptibility in under 40 minutes

McGill researchers have developed a diagnostic system capable of identifying bacteria—and determining which antibiotics can stop them—in just 36 minutes, a major advance in the global effort to curb antimicrobial resistance (AMR). Current clinical testing methods typically take 48 to 72 hours, leaving physicians without timely guidance.

The researchers say this innovation arrives at a critical moment due to the urgency of the AMR crisis, which arises from bacteria developing resistance to antibiotics.

“We are losing the race against antimicrobial resistance,” said Sara Mahshid, associate professor in the Department of Bioengineering and lead author on the Nature Nanotechnology study. “Every year, more than one million people die, more than from HIV/AIDS or malaria, and delayed treatment is a major driver. Rapid testing isn’t a luxury; it’s the missing link between diagnosis and survival.”

Platinum nanostructure sensor can differentiate mirror-image volatile scent compounds

Terpenes are volatile organic compounds that are responsible for, among other things, the typical scents of plants, resins or citrus fruits. These compounds occur naturally in the environment and influence chemical processes in the atmosphere. At high concentrations, they can irritate the respiratory tract and contribute to the formation of harmful derivatives. Many terpenes exist in two mirror-image forms, known as enantiomers, which can differ significantly in terms of their effects and how they are perceived—but which are difficult to distinguish between using technical means.

Now, researchers from the Department of Chemistry at the University of Basel have presented a new approach that allows these mirror-image forms of the molecules to be detected specifically.

“Our work focused on a specially developed platinum-based molecule that works as a sensor,” explains Dr. Annika Huber, first author of the study and a former doctoral student at the Swiss Nanoscience Institute’s Ph.D. School. “This sensor molecule has a fixed, three-dimensional shape and aggregates with a large number of identical molecules to form tiny stack-like nanostructures that react differently to the two mirror-image forms of the terpenes.”

From cryogenic to red-hot: Optical temperature sensing from 77 K to 873 K

An international collaboration involving researchers from the University of Innsbruck has developed a novel luminescent material that enables particularly robust and precise optical temperature sensing across an exceptionally broad temperature range.

Optical luminescence thermometry has been gaining increasing attention, as it allows contactless temperature measurement even under extreme conditions. A key concept in this field is so-called ratiometric Boltzmann thermometry, in which the intensity ratio of two thermally coupled emission transitions directly follows the temperature. The performance of such thermometers crucially depends on the electronic structure of the luminescent ion and its incorporation into the host structure.

In a recent study, the two first authors, Gülsüm Kinik from the research group of Prof. Markus Suta at Heinrich Heine University Düsseldorf and Ingo Widmann from the research group of Prof. Hubert Huppertz at the Department of General, Inorganic and Theoretical Chemistry at the University of Innsbruck, reported the compound Al0.993 Cr0.007 B4 O6 N, which stands out as an exceptionally high-performance luminescence thermometer. The material is based on Cr3+ ions embedded in an almost ideal octahedral coordination environment, resulting in a particularly well-defined energy level scheme.

AI Faces Fool Most of Us, But 5 Minutes of Training May Help You Spot Fakes

AI image generators have become remarkably proficient in a very short period, capable of creating faces that are considered to be more realistic than the real thing.

However, a new study points to a way that we can improve our AI-face detection capabilities.

Researchers from the UK tested the face-assessing capabilities of a group of 664 volunteers, consisting of super-recognizers (who have shown a high level of skill for comparing and recognizing real faces in previous studies), and people with typical face-recognition abilities.

‘Zombie’ Remnants of COVID-19 Hunt In Packs And Kill Our Immune Cells

‘Zombie’ coronavirus fragments not only help drive inflammation in long-COVID, but also destroy our immune cells.

A recent study by an international team of more than 30 authors reveals how the destruction of the virus within our body leaves dangerous protein fragments that target specific immune cells, which may explain some of the debilitating consequences millions of people with long-COVID now face.

“These fragments target a specific kind of curvature on the membranes of cells,” explains bioengineer Gerard Wong from the University of California, Los Angeles. “Cells that are spiky, that are star-shaped, or that have lots of tentacles end up getting preferentially suppressed.”

Silicon as strategy: the hidden battleground of the new space race

In the consumer electronics playbook, custom silicon is the final step in the marathon: you use off-the-shelf components to prove a product, achieve mass scale and only then invest in proprietary chips to create differentiation, improve operations, and optimize margins.

In the modern satellite communications (SATCOM) ecosystem, this script has been flipped. For the industry’s frontrunners, custom silicon is the starting line where the bets are high, and the rewards are even higher, not a late-stage luxury. Building custom silicon is just a small piece of the big project when it comes to launching a satellite constellation and the fact there are very limited off the shelf options.

The shift toward custom silicon is no longer a theoretical debate; it is a proven competitive requirement. To monetize the massive capital expenditure of a constellation, market leaders are already driving aggressive custom silicon programs for beamformers and modems from the very beginning. The consensus is clear: while commercial off-the-shelf (COTS) and field-programmable gate arrays (FPGAs) served as useful stopgaps, they have become a strategic liability that compromises price and power efficiency. If the industry is to scale to the mass market, operators must commit to bespoke silicon programs now — or risk being permanently priced out of the sky by competitors who have already optimized their hardware for the unit economics of space.

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