The World Institute of Kimchi (President: Hae Choon Chang), a government-funded research institute under the Ministry of Science and ICT, has confirmed in a new study that bacteriophages, which were previously considered a ‘nuisance’ during the process of food fermentation, actually play the role of key helpers that facilitate the survival of lactic acid bacteria (LAB).

Silicon, aluminium and lithium are the most critical raw materials on the planet. Their scarcity and the complexity of extracting them could hinder the development of technologies that are key to the green transition. This is one of the main conclusions of a study by the INGENIO Institute, a joint centre of the Spanish National Research Council (CSIC) and the Universitat Politècnica de València (UPV), in collaboration with the University of Rome, the Universitat de València and the University of London.

Addressing the urgent demand for clean energy, Japanese researchers utilized a single-step solution plasma process to synthesize high-performing, cost-effective, bifunctional catalysts for metal–air batteries. Their cobalt-tin hydroxide/carbon composites rival traditional platinum- and ruthenium-based materials in both performance and long-term stability. This breakthrough significantly lowers manufacturing costs and enhances the scalability of next-generation batteries, poised to accelerate the transition away from fossil fuels.

In bustling megacities worldwide, smart trains and railways are emerging as heroes in the fight against gridlocked traffic and choking pollution. Powered by cutting-edge tech like IoT and digital connectivity, these systems promise faster, greener, and more personalized travel—think seamless real-time updates and automated operations. Yet, this digital evolution brings a dark side: heightened vulnerability to cyberattacks that could derail operations, compromise passenger safety, or expose sensitive data. As railways go "smart," the need for robust cybersecurity isn't just a tech add-on—it's essential to unlock the full potential of a connected world without inviting chaos. This study dives into the cybersecurity maze of smart railways, mapping out threats, vulnerabilities, and defenses to ensure these vital infrastructures remain resilient in an increasingly hostile digital landscape.

Qing Wei and colleagues from the College of Engineering, China Agricultural University, systematically elaborated on the innovative applications of neural networks in agricultural product drying, offering new insights to address industry pain points. The related article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025620).

A review by Professor Abdul Shukor JURAIMI’s team from Universiti Putra Malaysia points out that hyperspectral imaging technology boasts advantages of non-contact operation, high precision, and early detection. Compared with traditional manual visual inspection, it can complete detection within 10–30 days after rice sowing—a critical period when weeds are most competitive—with an identification accuracy generally exceeding 90%. For example, regarding Echinochloa crus-galli and weedy rice (Oryza sativa f. spontanea), the most common weeds in rice fields, researchers achieved identification accuracies of 100% and 92%, respectively, by analyzing spectral data with intelligent algorithms. This accurate identification lays the foundation for targeted weeding: combined with UAVs and prescription mapping technology, it enables site-specific herbicide application, reducing pesticide usage by up to 50%. This not only cuts costs but also alleviates environmental burdens. The relevant article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025619).

A research team led by NIMS has, for the first time, produced nanoscale images of two key features in an ultra-thin material: twist domains (areas where one atomic layer is slightly rotated relative to another) and polarities (differences in atomic orientation). The material, monolayer molybdenum disulfide (MoS₂), is regarded as a promising candidate for use in next-generation electronic devices. This breakthrough was achieved by combining scanning transmission electron microscopy (STEM) with artificial intelligence (machine learning), allowing researchers to capture highly detailed nanoscale features over large areas. The research was published in Small Methods on August 6, 2025.