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.

Using a combination of telescopes, including the International Gemini Observatory, funded in part by the U.S. National Science Foundation and operated by NSF NOIRLab, and the SOAR telescope at Cerro Tololo Inter-American Observatory in Chile, a Program of NSF NOIRLab, astronomers have characterized the closest supernova linked to a fast X-ray transient. The observations reveal that these bright blasts of X-rays may be the result of a ‘failed’ explosive death of a massive star.

“Space ice” contains tiny crystals and is not, as previously assumed, a completely disordered material like liquid water, according to a new study by scientists at UCL (University College London) and the University of Cambridge.

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.