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.

What has been the trajectory of water erosion model research in China? What are the most widely used models currently? What are the characteristics of research distribution across different regions? What shortcomings need urgent attention? Professor Qingfeng Zhang from Northwest A&F University, along with researchers from multiple institutions, systematically reviewed the research progress of water erosion models in China from 1982 to 2022 using a combination of bibliometric and statistical analysis methods, providing a panoramic perspective to answer these questions. Relevant study has been published in the journal Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024580).

Professor Xinzhang Song (Zhejiang A&F University) et al. conducted a study that revealed the response of soil CO₂ emissions in bamboo forests of humid regions to straw mulching and its long-term effects. The research found that straw mulching not only significantly increased soil carbon emissions in the short term but also had enduring effects that persisted for at least three years after the removal of the mulching material. The study has been published in the journal Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025607).

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).

A team at TU Wien has discovered a surprising new way to turn heat into electricity—using metals instead of traditional semiconductors. By creating a kind of “electron traffic jam,” they achieved an unusually strong thermoelectric effect, potentially unlocking a powerful new method for converting waste heat from industry into usable power. This unexpected twist on a centuries-old principle could mark a turning point in energy technology.

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”.