In recent years, digital agricultural technology extension services (DATES), leveraging Internet platforms such as WeChat official accounts and mobile applications, have gained popularity, providing a new pathway for agricultural technology dissemination. This service overcomes the temporal and spatial limitations of traditional agricultural technology extension, enabling farmers to conveniently access planting knowledge. Then, can DATES effectively encourage farmers to adopt OMF and contribute to the green transformation of agriculture? Professor Minjuan Zhao from the College of Economics and Management, Northwest A&F University, and her team addressed this question through a survey of farmers in major apple - producing areas in China. The related research has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024590).

Dynamic multi-robot task allocation (MRTA) requires real-time responsiveness and adaptability to rapidly changing con ditions. Existing methods, primarily based on static data and centralized architectures, often fail in dynamic environments that require decentralized, context-aware decisions. To address these challenges, this paper proposes a novel graph reinforce ment learning (GRL) architecture, named Spatial-Temporal Fusing Reinforcement Learning (STFRL), to address real-time distributed target allocation problems in search and rescue scenarios. The proposed policy network includes an encoder, which employs a Temporal-Spatial Fusing Encoder (TSFE) to extract input features and a decoder uses multi-head attention (MHA) to perform distributed allocation based on the encoder’s output and context. The policy network is trained with the REINFORCEalgorithm.Experimentalcomparisonswithstate-of-the-artbaselinesdemonstratethatSTFRLachievessuperior performance in path cost, inference speed, and scalability, highlighting its robustness and efficiency in complex, dynamic environments.

A research team led by Professor Yumei Zhang from the Academy of Global Food Economics and Policy (AGFEP) at China Agricultural University, in collaboration with Dr. Issa Ouedraogo from the Alliance of Biodiversity International and International Center for Tropical Agriculture (CIAT), has conducted a study that reveals the structural characteristics and emission reduction potential of agrifood system carbon emissions in China and Africa. The related article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025609).

Recently, a research team led by Gengjie Jia at the Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, in collaboration with Yu Li at The Chinese University of Hong Kong, Xin Gao at King Abdullah University of Science and Technology, and Andrey Rzhetsky at The University of Chicago, published an article in Quantitative Biology titled “An effective encoding of human medical conditions in disease space provides a versatile framework for deciphering disease associations.” The study presents an embedding-based approach that encodes medical records into a high-dimensional disease space, providing a versatile framework for uncovering disease associations, facilitating genetic parameter estimation, and enabling data-driven disease classification. The authors also discuss the key challenges and future prospects of this emerging paradigm.

Dr. Kai Wang and his team published a review in Life Medicine to summarize the iPSC modification strategies to reach immune escape and reduce graft versus host disease (GvHD).

An Osaka Metropolitan University-led research team studied DNA interstrand crosslinks formation and its associated repair pathways to investigate whether colibactin-induced interstrand crosslinks play a crucial role in genotoxicity. 

A pioneering study reveals that low-grade coal, known as lignite, can be effectively converted into a resource for environmental protection—offering solutions for wastewater cleanup and resource recycling. Researchers from the University of Melbourne, in collaboration with Deakin University and Yancheng Institute of Technology, have developed a fast and energy-efficient method to modify lignite, greatly boosting its ability to strip toxic cadmium from polluted water.

Efficient nitrogen use is vital for sustainable tea cultivation and quality improvement. 

Excessive fluoride accumulation in tea leaves poses a potential threat to consumer health.