A comprehensive review published in Food & Medicine Homology highlights the transformative potential of time-resolved fluoroimmunoassay (TRFIA) as a fast, sensitive, and practical method for detecting pesticide residues in foods.

To overcome the challenge of insufficient loss strength in single-phase high-entropy ferrites, this work develops a novel defect-engineering-driven dual-phase strategy to fabricate spinel/rock-salt structured (Fe₀.₅Mg₀.₅CoNiCuMn)₃O₄@CuO composite ceramics. Combined experimental characterization and first-principles calculations demonstrate a strong positive correlation between defect concentration and microwave absorption performance. The optimized material achieves outstanding electromagnetic absorption with a minimum reflection loss of -48 dB and an effective absorption bandwidth of 3.9 GHz in the X-band. Remarkably, this work obtains 70% bandwidth retention after 1200 °C oxidation and a thermal conductivity of 2.154 W·m⁻¹·K⁻¹, demonstrating exceptional high-temperature stability and thermal management capability. This study pioneers a new pathway for the development of oxidation resistance and electromagnetic protection materials through defect-engineering-driven synergistic modulation.

The challenge of resource allocation for UAV swarms in dynamic and uncertain electromagnetic environments has been investigated for years. In a recent breakthrough published in the Chinese Journal of Aeronautics, a novel intelligent decision-making framework that addresses incomplete interference information has emerged. This innovative framework integrates fuzzy logic for uncertainty modeling, dynamic constrained multi-objective optimization, and transfer learning, enabling UAV swarms to achieve autonomous and efficient spectrum allocation under rapidly changing conditions while maintaining both communication performance and security.

Researchers at Tsinghua University developed PriorFusion, a unified framework that integrates semantic, geometric, and generative shape priors to significantly improve the accuracy and stability of road element perception in autonomous driving systems. The research addresses a long-standing challenge: existing end-to-end perception models often generate irregular shapes, fragmented boundaries, and incomplete road elements in complex urban scenarios.

Ride-pooling is widely recognized as a sustainable way to ease congestion, reduce costs and cut emissions, yet adoption remains limited.  When operators act independently, efficiency is low because requests cannot be matched across platforms.  Aggregation platforms seek to improve this by forcing all operators into a permanent coalition, but differences in size, cost and market position make such arrangements unstable.  To address this, researchers from Beihang University and Delft University of Technology developed a multi-level coalition formation game framework that enables coalitions to form dynamically in response to trip requests, allowing flexible cooperation without requiring all operators to remain in a single group at all times.

To answer this question: How to make AI truly scalable and reliable for real-time traffic assignment? A research team from KTH Royal Institute of Technology, Monash University, Technical University of Munich, Southeast University, and the University of Electro-Communications has developed a new framework—MARL-OD-DA—that offers a promising answer. The approach redesigns learning agents at the origin–destination (OD) level and utilizes Dirichlet-based continuous actions to achieve stable and high-quality solutions under dynamic travel demand.

To address the trade-off between accuracy and cross-city generalization in traffic flow estimation, a research team from The Hong Kong Polytechnic University and New York University proposes a novel framework based on global open multi-source (GOMS) data, including urban structures and population density. By developing an advanced graph neural network model that effectively fuses these static urban features with dynamic traffic data, the study achieves stable and accurate network-wide traffic estimation, as validated across 15 diverse cities in Europe and North America.

Researchers at National University of Singapore used multiple interpretable machine learning methods to predict traffic congestion in in Alameda County in the San Francisco Bay Area, USA, during the pre-lockdown, lockdown, and post-lockdown periods.