KAIST controls where 2D semiconductor crystals begin to grow, addressing a key challenge in stacking AI chips
Peer-Reviewed Publication
Updates every hour. Last Updated: 23-Oct-2026 22:16 ET (24-Oct-2026 02:16 GMT/UTC)
Professor Kibum Kang from the Department of Materials Science and Engineering, in collaboration with UNIST (President Chong-Rae Park), Hanyang University (President Ki-jeong Lee), and TDS Innovation (Co-CEOs Kibum Kang and Sanghyun Mo), has developed a new growth technique that precisely controls the location of nucleation, the stage at which 2D semiconductor crystals first form, using chemical species released from an oxide barrier.
Nanyang Technological University, Singapore (NTU Singapore) and the Institute of Mental Health (IMH) have developed a novel artificial intelligence (AI) framework capable of identifying youths at high risk of psychosis - a severe mental health condition that typically emerges in adolescence to early adulthood where a person may experience hallucinations, delusions, and disordered thinking - from short samples of casual, spontaneous speech.
In the International Journal of Extreme Manufacturing, Prof. Yang Li at Zhejiang University and his co-workers developed a dual-sided stencil printing technique that packs high-capacity energy storage into electrode tracks just 80 μm wide by giving a tungsten stencil two distinct physical personalities—a dual-sided "Janus mask" named after the two-faced Roman god.
Cardiovascular medicine is inherently multimodal, but the data available at the clinical decision point are rarely as complete or as synchronized as benchmark evaluations assume. Recent reviews identify incomplete datasets, heterogeneous data characteristics, and missing modalities as persistent barriers to multimodal clinical AI. A correspondence published in Medicine Discovery argues that the central problem is not how to fuse more data, but how to reason when clinically relevant data are incomplete, temporally misaligned, or no longer representative of the patient's current state. The authors distinguish five linked but non-equivalent problems — conventional multimodal fusion, robustness to missing modalities, temporally aware modeling, uncertainty-aware selective prediction, and clinically informed interpretation of why a modality is unavailable — and treat modality availability as an observation state rather than a data defect. They define deferral operationally, separate it from calibration, uncertainty estimation, and distribution-shift detection, propose hybrid deferral rules that combine non-compensatory clinical hard stops with graded model-derived signals, and set out five candidate conditions for an intended-use-specific minimum evaluation suite. The framework is offered as a complement to existing clinical AI guidance (TRIPOD+AI, PROBAST+AI, FUTURE-AI, DECIDE-AI, CONSORT-AI, SPIRIT-AI) rather than a replacement, and as a conceptual proposal requiring prospective evaluation and multi-stakeholder consensus.
KAIST, Caltech, and Nanyang Technological University (NTU) in Singapore develop “BeetleBot,” a soft robot based on artificial muscles. The robot combines walking and object gripping with visual and tactile sensing, so users can steer it while checking camera images and contact information. In demonstrations, the robot avoided obstacles and picked up and carried a target object, which lays the groundwork for soft robots used in remote tasks.
Researchers have developed a new electrochemical sensor for rapidly and accurately detecting a naturally occurring compound widely used in the food, beverage and fragrance industries.