New 'optical neural engine’ solves partial differential equations
Peer-Reviewed Publication
Updates every hour. Last Updated: 23-Aug-2025 16:11 ET (23-Aug-2025 20:11 GMT/UTC)
University of Utah engineers encode partial differential equations in light and feed them into newly designed optical neural engine, or ONE, to accelerate machine learning.
While studying embryos in vitro can have many ethical and technical complications, there is a model that serves as an excellent substitute. Two-dimensional “gastruloids” are made from colonies of human pluripotent stem cells that can replicate the third week of gestation in which the three germ layers of the body are established. In APL Bioengineering, researchers discuss a sorting system comprised of a microscope, a camera, a sorting stage, and devices for collecting and releasing the microrafts that the gastruloids are grown on. The system is controlled by custom software that automates the process.
In Nanotechnology and Precision Engineering, researchers developed a microrobot capable of manipulating small droplets in the presence of magnetic fields. To make their robot, they mixed neodymium magnetic particles and sugar with a chemically stable polymer. The sugar was then dissolved away, leaving holes throughout the polymer for increased surface area. Lastly, the team treated the polymer with plasma to make it attract water and other liquids. Including the magnetic particles allowed the team to control their robot by applying magnetic fields, and using powerful neodymium particles made the robot more responsive and effective compared to existing magnetic microrobots.
Researchers have experimentally observed the enhancement of neutron-rich particle emission from out-of-fission-plane in Fermi energy heavy ion collisions, suggesting a new probe for nuclear equation of state. The fast-rotating reaction system provides a beneficial environment to study the mechanisms of isospin migration and fission dynamics.
Topics range from liver diseases and transport planning to marine microorganisms / €177 million in funding for the initial funding period