Substrate inhibition is a longstanding bottleneck in microbial fermentation, particularly in lactic acid bacteria, where excessive substrate concentrations can paradoxically suppress growth and product formation. Conventional models, such as Haldane type formulations, are widely used but remain largely phenomenological, limiting their interpretability and predictive power for process optimization. Bridging this gap requires models that not only fit experimental data but also capture the underlying biological mechanisms.

Wearable smart sensors for monitoring human motion have emerged as an active area of research due to their potential applications in healthcare, sports performance, and human–machine interaction. These devices must not only provide accurate sensing capabilities but also be flexible, portable, and comfortable to integrate seamlessly into daily life. Conventional textiles, while widely used, are no longer sufficient to meet the functional demands of contemporary users. As a result, the textile industry is evolving toward functionalization and intelligence, with smart fabrics gaining increasing attention.

Layered double hydroxides (LDHs) exhibit excellent catalytic performance due to their unique two-dimensional (2D) layered structure, adjustable chemical composition, and tunable interlayer ion types and contents. However, when employed as a catalyst, LDH materials still suffer from several drawbacks, such as poor electrical conductivity and a tendency to agglomerate. To further improve their catalytic performance, a variety of effective modification strategies have been adopted, including electronic structure modulation, morphological control, and interface engineering.

The research team of Professor Kyoungjae Lee of the Department of Statistics at Sungkyunkwan University, through joint research with Professor Won Chang of Seoul National University and Professor Xuan Cao of the University of Cincinnati, developed Bayesian inference for the hidden dependence structures of multi-group high-dimensional data.

A research team has mapped how artificial intelligence is beginning to reshape cardiac arrest care, from early warning and emergency response to cardiopulmonary resuscitation and recovery after survival. By reviewing 114 studies involving more than 9.57 million patients, the study shows that artificial intelligence is most often used to predict cardiac arrest, guide resuscitation decisions, and estimate post-arrest outcomes. The findings suggest that artificial intelligence could become a practical tool for identifying high-risk patients earlier, supporting time-critical decisions during resuscitation, and improving post-arrest care, while also highlighting the need for stronger real-world validation before these tools can be widely adopted in emergency medicine.