Detecting possible extraterrestrial (ET) life on other planets in and away from our Solar System just got simpler through a machine learning (ML) method that could be used more commonly in future space missions. Scientists from Carnegie Institution’s Geophysical Laboratory and George Mason University have developed a new ML system that can potentially detect ET life with up to 90% accuracy. NASA’s Johnson Space Flight Center in 1996 announced finding evidence of microscopic fossil life in a Martian meteorite (coded ALH84001) which was recovered in Antarctica.
Also, since the 1950s, scientists have detected in space what they felt were components needed for life, which indicated the presence of ET life. But what evaded conclusive proof was putting a finger on whether it was of biological origin (residues of actual living creatures) or were they a result of some abiotic processes over time. Without accurately being convinced of that, it would be difficult to shout from the rooftops that we have indeed found any sign of ET life. However, the new machine learning system developed by the scientists at Carnegie Institution will change all that.
The scientists had initially trained their machine learning method to detect only abiotic and biotic signatures. But the method developed a third: living biotic, as against fossil biotic — which means it is capable of detecting fossil samples from more recent biological samples. This also opens up possibilities of the method distinguishing between photosynthetic life or life with cells with a nucleus while detecting ET life. Scientists feel this is the beginning of a new and exciting era in ET life detection. The artificial intelligence-based system could find its way in any number of future outer space-destined missions, and while carrying out its other experiments, could keep a lookout for surefire ET life.
‘ANTI-GRAVITY’ SAND GRAINS’ NEW BENEFITS FOR FUTURE
Engineering researchers at Lehigh University in eastern Pennsylvania, USA, have discovered that sand can actually flow uphill through an applied mechanism that could provide multiple benefits in various industries, including health care. They found when the sand grains were applied a force that enabled them to rotate on their axis — or torque — along with a magnet in an appropriate manner, sand grains actually flowed uphill. The find was completely by chance when one of the researchers discovered it during a research. When he rotated a magnet beneath a vial of iron oxide-coated polymer particles, called micro-rollers, the grains began to heap uphill.
The researchers then studied how the sand grains reacted to the magnet under different conditions. When they poured the micro-rollers without activating them with the magnet, they flowed downhill. But when they applied torque using the magnets, each particle began to rotate, creating temporary doublets that quickly formed and broke up. The result was cohesion that generated a negative ‘angle of repose’ due to a negative coefficient of friction. An angle of repose of a mound of granular material is the angle of the slope of the mound to the horizontal plane it is kept on.
The researchers found that by increasing magnetic force the cohesion among the sand grains increased. This enabled higher traction and the ability to move faster. The collective motion of all those grains, and their ability to stick to each other, allowed sand particles to essentially work together to do counterintuitive things like roll up the walls. The team is now buoyant with hopes of future applications using micro-rollers and magnets to mix grains, segregate materials, or move objects. They are looking at future uses in micro-robotics and in turn for healthcare.