Recently, Xie Zhen's team from Tsinghua University published a research article titled "SPECIFIC: A systematic framework for engineering cell state-responsive synthetic promoters reveals key regulators of T cell exhaustion" in Quantitative Biology, proposing an integrated framework combining chromatin accessibility analysis and machine learning for rational design of synthetic promoters.

Diswandi Nurba from IPB University in Indonesia at al. systematically evaluated the performance of four aeration system designs through a combination of Computational Fluid Dynamics (CFD) simulations and AHP-TOPSIS multi-criteria decision analysis, providing a scientific answer to this problem. The study had been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024577).

An international team from countries including Iran, Iraq, Uzbekistan, and India has co-authored a review paper published in the journal Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024564). The corresponding author is Dr. Mohammad MEHDIZADEH from University of Mohaghegh Ardabili. The article outlines the potential applications of machine learning technology in weed management and provides insights for addressing the aforementioned issues. In simple terms, machine learning acts like an “intelligent brain” for farmland——it can analyze vast amounts of agricultural data, automatically identify patterns, and make precise decisions, shifting weed control from a “broad net” approach to “precision strikes”.

Recently, Dr. Muhammad Waqar Akram and his team from the Department of Farm Machinery and Power at University of Agriculture Faisalabad in Pakistan developed a “Machine Vision-Based Automatic Fruit Grading System”, offering a new solution. The related article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2023532).

Dr. Roaf Ahmad Parray from ICAR-indian agricultural research institute (ICAR-IARI) and his colleagues provide an answer in a study published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024572). In this research, an international team of scientists from India, Denmark, and the United States developed an innovative technology integrating spectral sensors, machine learning models, and an intelligent spraying system, successfully applying it to control black rot disease in cauliflower. This technology, comprising three core components—non-destructive detection, intelligent decision-making, and targeted pesticide application—significantly reduces pesticide use and offers new insights for green agriculture.

The latest annual meeting for the Global Education Deans Forum brought together 53 representatives from 40 institutions across 29 countries in Shanghai and Lijiang, China. An article published online in ECNU Review of Education on May 27, 2025, attempts to  capture how a group of global education leaders view the promise and perils of AI amidst a rapidly changing educational landscape.