Nocturnal moths rely on hearing to escape predatory bats, yet how they respond to different bat calls has remained unclear. In a new study, researchers from Japan exposed moths to ultrasonic pulses simulating bat hunting stages and observed their flight and egg-laying behaviors. The study found that moths change how they fly and lay eggs depending on the level of danger, a finding that could help develop ultrasonic methods to protect crops from moth damage.

Approximate unlearning removes irrelevant information from vision-language models (VLMs) while preserving performance. However, current approaches are based on class unlearning, which excludes domain-specific recognition and is insufficient for practical applications. Researchers from Tokyo University of Science and National Institute of Advanced Industrial Science and Technology have proposed approximate domain unlearning as a novel approach to differentiate between domains. This innovation outperforms state-of-the-art alternative tuning techniques for VLMs, paving the way for practical and fine-grained unlearning.

The gerotor tooth profile is crucial for determining hydraulic system performance in automotive engineering. In a new development, researchers from Pusan National University have leveraged conditional generative adversarial networks for machine learning-driven gerotor profile synthesis and optimization. The novel approach has remarkably produced designs that outperform human efforts and lead to 32% more efficient hydraulic pumps, potentially revolutionizing the automotive industry.

Using sound to get objects to float works well if a single particle is levitated but it causes multiple particles to collapse into a clump in mid-air. Physicists at the Institute of Science and Technology Austria (ISTA) have now found a way to keep them apart using charge. Their findings, published in PNAS, could benefit materials science, robotics, and microengineering.