And that’s why planetary scientists are also interested in finding deltas on Saturn’s moon Titan. Titan is the only planetary body in the solar system besides Earth that has liquid currently flowing across its surface, so its deltas could be a scientific treasure trove.

One problem: Titan appears to be largely devoid of deltas, a new study finds, despite its large rivers of liquid methane and ethane.

“It’s kind of disappointing as a geomorphologist because deltas should preserve so much of Titan’s history,” said Sam Birch, an assistant professor in Brown University’s Department of Earth, Environmental and Planetary Sciences who led the work.

But the absence of deltas raises a host of new questions.

“We take it for granted that if you have rivers and sediments, you get deltas,” Birch said. “But Titan is weird. It’s a playground for studying processes we thought we understood.”

Titan is the largest of Saturn’s 274 confirmed moons. Its thick nitrogen and methane atmosphere gives rise to a host of Earth-like climate and weather features. Titan has clouds, wind and rain as well as rivers, lakes and seas. But instead of water, Titan’s fluid bodies contain methane and ethane, which are liquid at Titan’s chilly surface temperatures.

Scientists learned of Titan’s liquid bodies when the Cassini spacecraft flew by in 2006. Peering through Titan’s thick atmosphere with Cassini’s synthetic aperture radar (SAR), the spacecraft revealed spidering channels and large flat areas consistent with large bodies of liquid.

Largely missing from Cassini’s SAR images, however, were deltas — even at the mouths of large rivers. It wasn’t clear, however, whether the deltas were truly absent, or whether they just didn’t show up in Cassini’s SAR data. That’s the question Birch and his colleagues tried to answer with this new study, published in the Journal of Geophysical Research: Planets.

The problem with Cassini’s SAR data is that shallow liquid methane is largely transparent in any images. So while the SAR images could see the broad seas and river channels, it’s harder to confidently make out coastal features because it’s difficult to see where the coast ends and where the sea floor begins.

For the study, Birch developed a numerical model to simulate what Cassini’s SAR would see if it looked at a landscape scientists understand well: Earth. In the model, the water in Earth’s rivers and oceans was replaced by Titan’s methane liquid, which has different radar absorption properties compared to water.

“We basically made synthetic SAR images of Earth that assume properties of Titan’s liquid instead of Earth’s,” Birch said. “Once we see SAR images of a landscape we know very well, we can go back to Titan and understand a bit better what we’re looking at.”

The research found that the synthetic SAR images of Earth clearly resolved large deltas and many other large coastal landscapes.

“If there are deltas the size of the one at the mouth of the Mississippi River, we should be able to see it,” Birch said. “If there are large barrier islands and similar coastal landscapes like those we see all along the U.S. Gulf Coast, we should be able to see those.”

But when Birch and his colleagues combed over the Titan images in light of their new analysis, they came up mostly empty. Aside from two probable deltas near Titan’s south pole, the rest of the moon’s rivers were entirely delta-free. The researchers found that only about 1.3% of Titan’s large rivers that terminate at coastlines have deltas. On Earth, in contrast, nearly every river of similar size has a delta.

It’s not entirely clear why Titan generally lacks deltas, Birch says. The fluid properties of Titan’s rivers should make them perfectly capable of carrying and depositing sediment. It could be, the researchers say, that sea levels on Titan rise and fall so rapidly that deltas are smeared across the landscape more quickly than they can be built up in a single spot. Winds and tidal currents along Titan’s coasts may also play an equally large role in preventing delta formation.

And missing deltas aren’t the only mystery raised by the new research. The new analysis of Cassini SAR data of Titan’s coasts revealed pits of unknown origin deep within lakes and seas. The study also found deep channels on the floors of the seas that seem to have been carved by river flows, but it’s not clear how they got there.

All of these surprises will require more research to fully understand, Birch says.

“This is really not what we expected,” Birch said. “But Titan does this to us a lot. I think that’s what makes it such an engaging place to study.”

Constructed using 22 small pressure sensors and fueled by small solar panels on the tops of shoes, the system offers real-time health tracking based on how a person walks, a biomechanical process that is as unique as a human fingerprint.

This complex personal health data can then be transmitted via Bluetooth to a smartphone for quick and detailed analysis, said Jinghua Li, co-author of the study and an assistant professor of materials science and engineering at The Ohio State University.

“Our bodies carry lots of useful information that we’re not even aware of,” said Li. “These statuses also change over time, so it’s our goal to use electronics to extract and decode those signals to encourage better self health care checks.”

It’s estimated that at least 7% of Americans suffer from ambulatory difficulties, activities that include walking, running or climbing stairs. While efforts to manufacture a wearable insole-based pressure system have risen in popularity in recent years, many previous prototypes were met with low energy limitations and unstable performances.

To overcome the challenges of their precursors, Li and Qi Wang, the lead author of the study and a current PhD student in materials science and engineering at Ohio State, sought to ensure that their wearable is durable, has a high degree of precision when collecting and analyzing data, and can provide consistent and reliable power, said Li.

“Our device is innovative in terms of high resolution, spatial sensing, self-powering capability, and its ability to combine with machine learning algorithms,” she said. “So we feel like this research can go further based on the pioneering successes of this field.”

The study was recently published in the journal Science Advances.

This team’s system is also made unique through its use of AI. Using an advanced machine learning model, the wearable can recognize eight different motion states, including static ones like sitting and standing to more dynamic movements such as running and squatting.

Additionally, since the materials the insoles are made of are flexible and safe, the device, much like a smartwatch, is low-risk and safe for continuous use. For instance, after the solar cells convert sunlight to energy, that power is stored in tiny lithium batteries that don’t harm the user or affect daily activities.

Because of the distribution of sensors from toe to heel, the researchers could see how the pressure on parts of the foot is different in activities such as walking versus running.

During walking, pressure is applied sequentially from the heel to the toes, whereas during running, almost all sensors are subjected to pressure simultaneously. In addition, during walking, the pressure application time accounts for about half of the total time, while during running, it accounts for only about a quarter.

In health care, the smart insoles could support gait analysis to detect early abnormalities associated with foot pressure-related conditions (such as diabetic foot ulcers), musculoskeletal disorders (such as plantar fasciitis) and neurological conditions (such as Parkinson’s disease).

The new system also used machine learning to learn and classify different types of motion. That offers opportunities for personalized health management, including real-time posture correction, injury prevention and rehabilitation monitoring. Customized fitness training may also be a future use, the researchers said.

According to the study, these smart insoles showed no notable deterioration in performance after 180,000 cycles of compression and decompression, showing their long-term durability.

“The interface is flexible and quite thin, so even during repetitive deformation, it can remain functional,” said Li. “The combination of the software and hardware means it isn’t as limited.”

Researchers expect the technology will likely be available commercially within the next three to five years. Next steps to advance the work will be aimed at improving the system’s gesture recognition abilities, which, according to Li, will likely be helped with further testing on more diverse populations.

“We have so many variations among individuals, so demonstrating and training these fantastic capabilities on different populations is something we need to give further attention to,” said Li.

Other co-authors include Hui Guan, Chen Wang, Peiming Lei, Hongwei Sheng, Huasheng Bi, Jinkun Hu, Chenhui Guo, Yichuan Mao, Jiao Yuan, Mingjiao Shao, Zhiwen Jin and Wei Lan from Lanzhou University in China.

Of course, wood has often been passed over in favor of other materials because it is easily damaged by sunlight and moisture when used outdoors. Wood coatings have been designed to protect wood surfaces for this reason, but coating damage often starts before it becomes visible. Once the deterioration can be seen with the naked eye, it is already too late.

To solve this problem, a team of researchers at Kyoto University is working to create a simple but effective method of diagnosing this nearly invisible deterioration before the damage becomes irreparable.

“If we can ‘see’ what the eye cannot, we can extend the life of wooden structures and improve sustainability in the building industry,” says corresponding author Yoshikuni Teramoto.

The team is endeavoring to bring data-driven tools into traditional wood maintenance by combining mid-infrared spectroscopy with machine learning. They’ve started by testing artificially weathered wood coatings along with coatings containing cellulose nanofiber, a plant-derived additive that can improve the durability of these coatings.

Their machine learning component uses a technique called partial least square, which they employed to build a model to predict the extent of deterioration. They also used a genetic algorithm to identify the most informative infrared signals, improving both accuracy and interpretability.

“We were surprised to find that very subtle chemical changes — far too small to detect visually — could be captured by infrared spectroscopy and predicted by the model,” says Teramoto.

This approach allows the researchers to detect subtle chemical changes and estimate the level of deterioration with high accuracy. By making it possible to diagnose early coating deterioration quickly and without damaging the wood, their method could also reduce the need for costly visual inspections by detecting early warning signs of deterioration and preventing further decay.

With their study, the researchers have also demonstrated how chemistry and data-driven modeling techniques can work together to support smarter maintenance of sustainable buildings. “We hope this technology will help bridge the gap between traditional craftsmanship and modern data science,” continues Teramoto.

The research team is now conducting tests on real wooden buildings, with plans to improve their model for application in new paint and coating product development.

Beyond wood, the team’s method may also be applied to materials like concrete or metal to unlock new possibilities for diagnosing other kinds of early material failure, improving the sustainability of other applications and industries in the process.