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).

As electric vehicles (EVs) zoom into the mainstream—thanks to pioneers like Tesla and a global push to ditch fossil fuels—the power grid is feeling the strain from surging charging demands. Traditional setups struggle with reliability, costs, and environmental impacts, especially when integrating intermittent solar photovoltaic (PV) systems. Enter the game-changer: EV charging stations (EVCS) fused with PV panels and battery energy storage systems (BESS). This research tackles the chaos by optimizing station placement and operations in distribution networks, ensuring efficient power flow while curbing emissions and expenses. By addressing these hurdles, it paves the way for sustainable transport that doesn't overload our aging grids, making EVs a practical choice for everyone from city commuters to long-haul drivers.

This study aimed to investigate the impact of Jinfeng Pills on the receptivity of a thin endometrium in rats and elucidate its mechanism of action. A thin endometrial model was established in female Sprague–Dawley rats. The rats were randomly assigned to the control, model, estradiol valerate, and Jinfeng Pill groups. Histological evaluation using hematoxylin and eosin staining was performed to assess morphological changes in the endometrium under light microscopy. Enzyme-linked immunosorbent assay was used to measure vascular endothelial growth factor (VEGF), estrogen, and progesterone levels in rat serum. Immunohistochemical analysis was used to examine morphological alterations in the endometrium. Immunofluorescence and quantitative polymerase chain reaction were employed to analyze the expression of VEGF, platelet endothelial cell adhesion molecule (CD31), β-catenin, leukemia inhibitory factor (LIF), and homeobox gene A10 (HOXA10) proteins and mRNA in endometrial tissue.

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.

Recently, Associate Professor Xiaolong Feng from the College of Economics and Management at China Agricultural University, together with researchers from the Alliance for a Green Revolution in Africa (AGRA), has addressed these questions through a comparative analysis of agricultural subsidy policies in China and Africa. The related article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025624).