As urban drone logistics becomes a practical reality, balancing economic cost, ground safety risk, and noise impact poses a systemic challenge. A recent study proposes a novel approach to design urban low-altitude logistics networks, incorporating noise constraints into a multi-objective optimization framework. By combining a layered network model with a dual-population co-evolutionary algorithm, the research provides a new direction for the low-altitude logistics infrastructure of future cities.

Solid polymer electrolytes (SPEs) have garnered considerable interest in the field of lithium metal batteries (LMBs) owing to their exceptional mechanical strength, excellent designability, and heightened safety characteristics. However, their inherently low ion transport efficiency poses a major challenge for their application in LMBs. To address this issue, covalent organic framework (COF) with their ordered ion transport channels, chemical stability, large specific surface area, and designable multifunctional sites has shown promising potential to enhance lithium-ion conduction. Here, we prepared an anionic COF, TpPa-COOLi, which can catalyze the ring-opening copolymerization of cyclic lactone monomers for the in situ fabrication of SPEs. The design leverages the high specific surface area of COF to facilitate the absorption of polymerization precursor and catalyze the polymerization within the pores, forming additional COF-polymer junctions that enhance ion transport pathways. The partial exfoliation of COF achieved through these junctions improved its dispersion within the polymer matrix, preserving ion transport channels and facilitating ion transport across COF grain boundaries. By controlling variables to alter the crystallinity of TpPa-COOLi and the presence of –COOLi substituents, TpPa-COOLi with partial long-range order and –COOLi substituents exhibited superior electrochemical performance. This research demonstrates the potential in constructing high-performance SPEs for LMBs.

Joint health is critical for musculoskeletal (MSK) conditions that are affecting approximately one-third of the global population. Monitoring of joint torque can offer an important pathway for the evaluation of joint health and guided intervention. However, there is no technology that can provide the precision, effectiveness, low-resource setting, and long-term wearability to simultaneously achieve both rapid and accurate joint torque measurement to enable risk assessment of joint injury and long-term monitoring of joint rehabilitation in wider environments. Herein, we propose a piezoelectric boron nitride nanotubes (BNNTs)-based, AI-enabled wearable device for regular monitoring of joint torque. We first adopted an iterative inverse design to fabricate the wearable materials with a Poisson’s ratio precisely matched to knee biomechanics. A highly sensitive piezoelectric film was constructed based on BNNTs and polydimethylsiloxane and applied to precisely capture the knee motion, while concurrently realizing self-sufficient energy harvesting. With the help of a lightweight on-device artificial neural network, the proposed wearable device was capable of accurately extracting targeted signals from the complex piezoelectric outputs and then effectively mapping these signals to their corresponding physical characteristics, including torque, angle, and loading. A real-time platform was constructed to demonstrate the capability of fine real-time torque estimation. This work offers a relatively low-cost wearable solution for effective, regular joint torque monitoring that can be made accessible to diverse populations in countries and regions with heterogeneous development levels, potentially producing wide-reaching global implications for joint health, MSK conditions, ageing, rehabilitation, personal health, and beyond.

A recent study investigates the contrasting patterns of symbiotic nitrogen fixation (SNF) and asymbiotic nitrogen fixation (ANF) along altitudinal gradients in subtropical forests. The research found that SNF rates declined with increasing altitude due to higher soil nitrogen availability and lower air temperatures, while ANF rates showed a hump-shaped pattern, influenced by soil properties at lower altitudes and climatic factors at higher altitudes. The study underscores the importance of distinguishing between SNF and ANF in ecological studies and Earth system models, providing valuable insights for improving global BNF estimates and refining model predictions.

The Vortex Particle Method (VPM) is a meshless vortex flow simulation approach gaining traction for its efficient simulation of unsteady vortex wakes evolution. However, traditional VPM has huge challenge on accurately simulating complex flows due to its poor numerical stability. Recently, a team of aviation researchers led by Min Chang from Northwestern Polytechnical University in China have developed a Stability-enhanced VPM (SEVPM). These advancements enable stable, high-fidelity simulations of complex flows. The researchers demonstrated that their SEVPM can accurately and stably simulate high Reynolds number flows and shear turbulence. The researchers plan to further validate and refine the Stability-enhanced VPM by applying it to more complex and realistic flow scenarios.

Aircraft conceptual design is a highly complex process involving multidisciplinary trade-offs and creative thinking. Recent advances in generative artificial intelligence (AI) provide promising opportunities to automate and augment this process. A new study, recently published in the Chinese Journal of Aeronautics, presents an AI-driven framework capable of generating aircraft configuration schemes based on design requirements, integrating aerodynamic knowledge and system constraints. This research fills a key gap in intelligent design methodology, offering a new tool to revolutionize the early stages of aircraft development.

High-resolution flow field data are critical for accurately evaluating the aerodynamic performance of aircraft. However, acquiring such data through large-scale numerical simulations or wind tunnel experiments is highly resource-intensive. Flow field super-resolution techniques offer an efficient alternative by reconstructing high-resolution data from low-resolution inputs. While existing super-resolution methods can recover the global structure of the flow, they often struggle to capture fine local details, especially shock waves. To address this limitation, this research proposes the FlowViT-Diff framework that integrates Vision Transformers (ViT) with an enhanced denoising diffusion probabilistic model to simultaneously capture global coherence and local flow features with high fidelity.

Maritime recovery of spacecraft is critical for crewed missions, offering advantages such as reduced impact forces and enhanced safety. While airbag cushioning systems have been widely adopted to mitigate landing impacts, prior studies predominantly focused on land or calm-water scenarios, leaving the complex interactions between airbags, reentry capsules, and ocean waves poorly understood. This study published in the Chinese Journal of Aeronautics on June6, 2025, addresses this gap by employing a Fluid-Structure Interaction (FSI) model to analyze water-landing characteristics under wave conditions, revealing key mechanisms such as wave-phase-dependent impact forces and horizontal velocity thresholds for stability. The findings provide essential insights for optimizing recovery systems, ensuring safer and more reliable maritime operations for reusable spacecraft.