An international team led by Associate Professor Kimihiko Nakajima of Kanazawa University has captured a rare look at the early universe. Using the James Webb Space Telescope (JWST *1) and the power of a gravitational lensing (* 2) in space, the team achieved a definitive characterization of LAP1-B, an ultra-faint galaxy from 13 billion years ago. Expanding upon initial detections, this new study utilized deep JWST spectroscopy to reveal a record-breaking low oxygen abundance (* 3) -- merely 1/240th that of the Sun. This chemically primitive state, coupled with an elevated carbon-to-oxygen ratio and a dominant dark matter halo, suggests that LAP1-B is the long-sought "ancestor" of the mysterious fossil galaxies found near our Milky Way today, providing a historic window into the earliest, most primitive stages of galaxy assembly.

With artificial intelligence pushing today’s hardware to process, move, and cool more, Penn physicists led by Bo Zhen are looking to the electron’s massless counterpart, the photon, to shoulder more of the load. In a new study, the team has created hybrid light-matter particles that interact strongly enough to compute, pointing toward ultrafast, low-energy optical AI hardware.

A retrospective study of 17 drug-resistant epilepsy patients found that stereo-electroencephalography-guided radiofrequency thermocoagulation (RF-TC) rapidly altered brain network connectivity after treatment. Reductions in alpha-band connectivity within epileptogenic regions were linked to clinical outcomes, suggesting that short resting-state recordings may help predict treatment response. Findings support RF-TC as a network-modulating therapy rather than a purely local lesioning procedure and highlight its potential role in guiding more personalized epilepsy care.

As environmental challenges continue to grow, effective and sustainable management increasingly depends on balancing institutional support with community-led action. Based on four decades of conservation in Tokyo’s Zushi-Onoji satoyama, a recent study conducted at Sophia University, Japan, identifies “gentle power” as a framework grounded in mutual trust, demonstrated expertise, and collaborative learning—offering a new pathway for sustainable environmental governance.

Researchers have introduced a novel canonical correlation guided deep neural network (CCDNN) to improve multi-source data acquisition and fusion. The model is designed to integrate heterogeneous data more effectively, addressing limitations of existing fusion methods in handling diverse and high-dimensional inputs. By leveraging advanced neural network structures, CCDNN enhances feature representation and decision-making accuracy. Experimental results demonstrate superior performance across benchmark tasks, highlighting its potential for applications in intelligent control, automation, and data-driven engineering systems.