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).