A 20-hectare plot at the Paint Rock ForestGEO site in north Alabama (29,282 trees mapped) reveals how landscape features shape tree species distribution and biomass. While overall biomass did not correlate with landform or topographic indices, the biomass of individual species did. The dominant species appeared to partition the site with American beech and yellow-poplar dominating the valleys, and white oak, southern shagbark hickory, and white ash predominantly on slopes and benches Average biomass was 211 Mg/ha., The species distribution demonstrates how topographic niche partitioning maximizes ecosystem carbon storage, as published in Forest Ecosystems.

During the preliminary design phase of flapping-wing micro air vehicles (FWMAVs), there currently exists a deficiency in rapid prediction method for the aerodynamic characteristics of flexible flapping wings. A novel aerodynamic prediction method for flexible flapping wings has recently achieved significant breakthroughs. This method innovatively employs conical surface to mimic wing deformation, combined with an unsteady panel method for aerodynamic force computation, enabling rapid and accurate prediction of both aerodynamic characteristics and control moments of flexible flapping wings.

Both biotic factors (microbial biomass and leaf nutrients) and abiotic factors (climate, soil properties, and elevation) play important roles in shaping how sensitive forest soil respiration (Q₁₀) is to temperature changes. By analyzing 766 soil Q₁₀ values from forests around the world, researchers found that microbial biomass carbon is the strongest single predictor, with plant traits like leaf phosphorus content also having a clear impact. The findings highlight the need to consider both biotic and abiotic influences when managing forests and improving carbon cycle models in a warming climate.

Unmanned Swarm Systems (USS) have transformed key fields like disaster rescue, transportation, and military operations via distributed coordination, yet trajectory prediction accuracy and interaction mechanism interpretability remain major bottlenecks—issues that existing methods fail to address by either ignoring physical constraints or lacking explainability. A recent breakthrough from Northwestern Polytechnical University solves this: Dr. Shuheng Yang and Prof. Dong Zhang developed the Swarm Relational Inference (SRI) model, an unsupervised end-to-end framework integrating swarm dynamics with dynamic graph neural networks. This model not only enhances interpretability and physical consistency but also drastically reduces long-term prediction errors, marking a critical step toward reliable autonomous collaboration for real-world USS applications.

For decades, aerospace engineers have confronted the life-threatening challenge of reigniting aircraft engines in high-altitude, low-pressure environments. Traditional spark ignition systems, limited by short discharge time and low energy efficiency, consistently fail to ignite lean kerosene-air mixtures in some difficult conditions. Although, gliding arc plasma ignitor offers improvements, its dependence on external gas sources prevents compatibility with combustor wide flight envelopes. This critical bottleneck has impeded next-generation aerospace propulsion systems.

Performance of Global Navigation Satellite Systems (GNSS) in providing positioning, velocity estimation, and timing services in urban environments often suffers significant degradation due to multipath effects and Non-Line-of-Sight signal reception. Traditional Fault Detection and Exclusion methods face technical bottlenecks, including high computational complexity and insufficient exclusion accuracy caused by the complex and diverse nature of fault modes. This study proposed a novel fault detection and correction method for Doppler-observable-based velocity estimation: GS-LASSO (Grouping-Sparsity Least Absolute Shrinkage and Selection Operator). Experiment results demonstrated that the GS-LASSO method could provide high-precision velocity estimates at the decimeter-per-second (dm/s) level in complex urban environments with limited computational resources.

A study published in Science China Earth Sciences (Issue 9, 2025) has quantitatively reconstructed changes in nitrate availability in the Early Triassic ocean by systematically integrating global nitrogen isotope records and applying a nitrogen cycle box model. The research reveals significant temporal evolution and spatial variability in nitrate availability during this period. By correlating multiple paleoenvironmental proxies, the study uncovers the underlying mechanisms of the evolution of nitrate availability and suggests that prolonged nitrate depletion likely played a key role in delaying the recovery of marine ecosystems after the end-Permian mass extinction. These findings provide new insights into the processes governing ecosystem recovery following major extinction events, offering a clearer understanding of past environmental challenges.

Researchers in China have developed a magnesia supported rhodium catalyst that enables the selective growth of ultrathin carbon nanotubes only 0.61 nanometer wide—the smallest stable nanotubes known.

An international research team has advanced an imaging method to capture nanoscale “spin maps” of chiral perovskites for the first time, revealing how these materials control electron spin at room temperature. The study also identifies a new type of spin-sensitive junction at the interface with metals. The findings, recently published in National Science Review, could guide the design of next-generation spintronic devices.