With the long-term on-orbit operation of the China Space Station, using nanosatellite swarms for cooperative observation of its external structures has become an important technical direction for enhancing in‑orbit monitoring capabilities. However, the space station itself has a non‑spherical complex geometry (e.g., protruding structures such as modules and docking ports) and may perform attitude maneuvers (e.g., sun‑pointing) during orbital flight, making it a dynamic obstacle with both intricate shape and rotational motion relative to the nanosatellites. Traditional artificial potential field obstacle avoidance methods mostly simplify obstacles as spheres or ellipsoids, which cannot accurately characterize the composite geometric features of the space station, and usually ignore the obstacle's own rotational motion. This leads to difficulties in constructing the repulsive potential field, limited maneuvering space, and, more critically, a lack of rigorous theoretical proof to guarantee closed-loop system stability and collision avoidance. Therefore, developing a nanosatellite formation control method that can handle rotating non‑spherical obstacles while providing strict stability guarantees is of significant theoretical importance and engineering value for ensuring safe fly-around and efficient cooperative observation in the vicinity of the space station.

Researchers who combine machine learning and economics are studying the social impact of algorithms that determine payouts on digital marketplaces. In a recently published study, the team highlights the structural imbalance in digital labor markets and provides a blueprint for fairer market with better working conditions for gig workers.

Hybrid nanomaterials (HNMs) have become more interesting to researchers for various optoelectronic and biological applications. In response, this investigation focuses on the impact of loading ratios of (0, 1 %, 3 %, and 5 %) of HNMs from graphene oxide (GO) and silicon nitride (Si₃N₄). HNMs are utilized to reinforce blended polymers, including polyethylene oxide (PEO), carboxymethyl cellulose (CMC), and nano-polyaniline (PANI) to fabricate (PEO_100K–CMC–PANI/GO–Si₃N₄) using the developed sol–gel-ultrasonic procedure. X-ray diffraction revealed semi-crystalline behavior among all samples, while Fourier transform infrared spectroscopy showed strong physical interfacial interactions among the sample components. Meanwhile, field emission scanning electron and transmission electron microscopies showed a fine dispersion and a homogeneous matrix with significant changes. The optical absorption behavior revealed continuous high absorption peaks at 200–280-nm wavelengths, which strongly impacts (GO–Si₃N₄). Increases in concentration also strongly impact (GO–Si₃N₄), which results in an improved optical energy gap for the allowed and forbidden transitions from 3.5 eV for the blended polymer to 3 and 2.9 eV by increasing the HNM content. The contributions of HNMs notably enhance the ability to reduce the zones of the bacteria, especially Escherichia coli, from 18 to 26 mm. In effect, HNMs with a concentration higher than 5 % assist in inhibiting the growth of lung cancer (A549) cells. As such, these NCs present good optical behavior for multi-applications, such as biosensors and biological and optoelectronic devices.