Recently, Associate Professor Xinglong Dai from Agronomy College of Shandong Agricultural University and his colleagues proposed a quantitative design theory and technical pathway for green yield increase and efficient nitrogen utilization in winter wheat, providing new insights to address this challenge. Related paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025631).

The microtubule organizing centers (MTOCs) of oocytes are essential for meiotic spindle assembly and for ensuring precise chromosome segregation. The detailed dynamic changes of MTOCs in germinal vesicle (GV) oocytes - a stage where early events of MTOC maturation happen - remain unclear. Recently, a study published in Science Bulletin explored the dynamics of MTOCs maturation in GV oocytes and disclosed the key factors involved in these processes. According to the investigation, MTOCs maturation is required for spindle assembly and may play an unrecognized role in oocyte aging.

Recently, a review paper conducted by Professor Jianchang Yang from Yangzhou University, et al. pointed out that optimizing the “harvest index” (the ratio of yield to total aboveground biomass) can achieve a synergistic enhancement of rice yield and resource utilization efficiency. The study found that the harvest index of modern rice varieties generally hovers around 0.5, but there is still room for improvement through the regulation of physiological traits. Key strategies include three main aspects: first, increasing the “grain-to-leaf ratio”, which refers to the number of grains per unit leaf area, balancing the relationship between photosynthetic products and grain demand; second, enhancing the “sugar-to-spikelet ratio”, which is the ratio of non-structural carbohydrates stored in the stem before flowering to the number of grains, providing more energy for grain filling; third, optimizing the “proportion of productive tillers” to reduce the consumption of water and nutrients by ineffective tillers, thereby improving population structure and light utilization. The related paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J–FASE–2025610).

Recently, a team of researchers led by Professor Peng Hou from the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences systematically summarized the limiting factors in corn production and proposed a green production scheme that balances high yield with efficient resource utilization based on quantitative design principles. The related paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025601).

The study introduces ISM-FLUX, a simpler version of the powerful MINFLUX concept. By using a detector array instead of a single element sensor, ISM-FLUX can precisely locate single molecules over a much larger area. This larger localisation range significantly increases the robustness of ISM-FLUX and dramatically simplifies the architecture by mitigating the need for an iterative localization approach, which will facilitate broader adoption of MINFLUX.

The rise of large-scale artificial intelligence (AI) models, such as ChatGPT, DeepSeek, and autonomous vehicle systems, has significantly advanced the boundaries of AI, enabling highly complex tasks in natural language processing, image recognition, and real-time decision-making. However, these models demand immense computational power and are often centralized, relying on cloud-based architectures with inherent limitations in latency, privacy, and energy efficiency. To address these challenges and bring AI closer to real-world applications, such as wearable health monitoring, robotics, and immersive virtual environments, innovative hardware solutions are urgently needed. This work introduces a near-sensor edge computing (NSEC) system, built on a bilayer AlN/Si waveguide platform, to provide real-time, energy-efficient AI capabilities at the edge. Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction, coupled with Si-based thermo-optic Mach–Zehnder interferometers for neural network computations, the system represents a transformative approach to AI hardware design. Demonstrated through multimodal gesture and gait analysis, the NSEC system achieves high classification accuracies of 96.77% for gestures and 98.31% for gaits, ultra-low latency (< 10 ns), and minimal energy consumption (< 0.34 pJ). This groundbreaking system bridges the gap between AI models and real-world applications, enabling efficient, privacy-preserving AI solutions for healthcare, robotics, and next-generation human–machine interfaces, marking a pivotal advancement in edge computing and AI deployment.

Rechargeable zinc-ion batteries have emerged as one of the most promising candidates for large-scale energy storage applications due to their high safety and low cost. However, the use of Zn metal in batteries suffers from many severe issues, including dendrite growth and parasitic reactions, which often lead to short cycle lives. Herein, we propose the construction of functional organic interfacial layers (OIL) on the Zn metal anodes to address these challenges. Through a well-designed organic-assist pre-construction process, a densely packed artificial layer featuring the immobilized zwitterionic molecular brush can be constructed, which can not only efficiently facilitate the smooth Zn plating and stripping, but also introduce a stable environment for battery reactions. Through density functional theory calculations and experimental characterizations, we verify that the immobilized organic propane sulfonate on Zn anodes can significantly lower the energy barrier and increase the kinetics of Zn²⁺ transport. Thus, the Zn metal anode with the functional OIL can significantly improve the cycle life of the symmetric cell to over 3500 h stable operation. When paired with the H₂V₃O₈ cathode, the aqueous Zn-ion full cells can be continuously cycled over 7000 cycles, marking an important milestone for Zn anode development for potential industrial applications.

The 2025 MRS International Risk Conference, jointly organized by China Finance Review International (CFRI), Suffolk University’s Sawyer Business School, and the Modern Risk Society (MRS), successfully concluded in Boston from 24 to 26 July 2025. The three-day conference united leading scholars and industry experts from around the world, emphasizing the importance of cutting-edge research in risk and finance.

Solid-state sodium batteries (SSSBs) are emerging as a promising alternative to conventional lithium-ion batteries, owing to their enhanced safety, cost-effectiveness as well as the abundance of sodium resources. However, despite their conceptual advantages, significant performance degradation, mainly associated to the electrode-electrolyte interfaces, has hindered their widespread application. A recent study led by researchers from the Beijing Institute of Technology provides a comprehensive mechanistic understanding of interfacial degradation in NASICON-type electrolyte-based solid-state sodium metal batteries. Their work focuses on Na₃Zr₂Si₂PO₁₂ (NZSP), a widely studied ceramic electrolyte known for its robust thermal stability and competitive ionic conductivity, yet plagued by poor long-term interfacial performance.

Scientists in Korea developed a photopatterning approach for emissive layer (EML) patterns using prepatterned photoresist based on a molecular crosslinking strategy. This approach enables ultra-high resolution up to 3000 ppi—fulfilling AR display requirements—without direct exposure of EML to etchants or UV irradiation, unlike conventional photolithography. The simple method offers a promising route for high-resolution OLEDs for VR/AR applications.