To answer this question: How to make AI truly scalable and reliable for real-time traffic assignment? A research team from KTH Royal Institute of Technology, Monash University, Technical University of Munich, Southeast University, and the University of Electro-Communications has developed a new framework—MARL-OD-DA—that offers a promising answer. The approach redesigns learning agents at the origin–destination (OD) level and utilizes Dirichlet-based continuous actions to achieve stable and high-quality solutions under dynamic travel demand.

A “standard reference thermoelectric module (SRTEM)*” for objectively measuring thermoelectric module performance has been developed in Korea for the first time. A research team led by Dr. Sang Hyun Park at the Korea Institute of Energy Research (KIER; President Yi, Chang-Keun) developed the world’s second standard reference thermoelectric module, following Japan, and improved its performance by more than 20% compared with existing modules, demonstrating the excellence of Korea’s homegrown technology.

To address the trade-off between accuracy and cross-city generalization in traffic flow estimation, a research team from The Hong Kong Polytechnic University and New York University proposes a novel framework based on global open multi-source (GOMS) data, including urban structures and population density. By developing an advanced graph neural network model that effectively fuses these static urban features with dynamic traffic data, the study achieves stable and accurate network-wide traffic estimation, as validated across 15 diverse cities in Europe and North America.

Researchers at National University of Singapore used multiple interpretable machine learning methods to predict traffic congestion in in Alameda County in the San Francisco Bay Area, USA, during the pre-lockdown, lockdown, and post-lockdown periods.

Researchers find that diversifying crops and integrating livestock improves farm efficiencies and ecosystem services in the US Midwest. 

Within an international collaboration, an experimental research team of LENS investigated vortex dynamics in strongly interacting superfluids using ultracold lithium gases. By precisely engineering and observing single quantized vortices, researchers uncovered how microscopic mutual friction governs superfluid dissipation. The study provides the first indirect experimental evidence of quasiparticles trapped in vortex cores, offering new insights relevant to superfluids, superconductors, and future quantum technologies.

Colloidal quantum dot (QD) light-emitting diodes have great potential in display applications. However, their commercialization remains a challenge due to the difficulty in achieving high-resolution patterning of QDs without degrading their optical properties. To address this, researchers have developed a nondestructive method for ultrahigh-resolution QD patterning. By blending QDs with a photocrosslinkable polymer, the approach preserves their optical properties and boosts efficiency and lifetime, paving the way for development of next-generation display technologies.

Electropulsing treatment rapidly heats metallic materials using high-density electrical pulses. Scientists at Pusan National University have now isolated its thermal and athermal effects in magnesium alloys using a novel T-type specimen. Their findings show that athermal effects significantly accelerate strain-induced boundary migration and grain growth—revealing how electropulsing can act as a fast, energy-efficient next-generation processing technology for lightweight metals.