With heavy reusable launch vehicles becoming a key technology for low-cost and high-frequency space exploration, cable-net recovery schemes have attracted considerable attention due to their strong adaptability to landing dispersion and attitude deviations. However, under multiple sources of interference—including engine plume impingement, trolley motion, and flexible vibrations—the dynamic state information of the flexible cable-net recovery system is difficult to acquire directly, severely constraining the accuracy of closed-loop control and capture reliability. Although Kalman filtering combined with multibody dynamics models can achieve state estimation for complex systems, the flexible cable-net system is inherently a high-dimensional, nonlinear, strongly rigid-flexible coupled constrained multibody system, whose dynamic equations are formulated as index-3 differential-algebraic equations (DAEs). Conventional methods require the conversion of DAEs into ordinary differential equations (ODEs) followed by linearization, a process that is complex and lacks generality; conventional numerical integration methods converge slowly when dealing with such stiff problems, with the Jacobian matrix becoming severely ill-conditioned under large time steps and constraints being prone to violation. Therefore, developing a filtering method that does not require model transformation, can directly utilize the multibody DAE for state prediction, and can maintain constraint satisfaction has become a key technical challenge for state estimation in flexible cable-net recovery.

Due to its unique orbital characteristics, the retrograde geosynchronous orbit (RGSO) has demonstrated substantial potential in the fields of space surveillance and space situational awareness. However, constrained by the direction of Earth's rotation and the tracking, telemetry, and control capabilities of launch sites, the direct launch of satellites into RGSO from Earth must overcome an additional velocity increment of more than approximately 4 km/s, entailing enormous fuel consumption and significant engineering difficulty. Conventional approaches employ lunar gravity assists to transfer from low Earth orbit or geosynchronous transfer orbit to RGSO, yet the velocity increment requirements remain relatively high and the transfer paths are limited. With the increasing development of cislunar space in recent years, an increasing number of spacecraft on cislunar orbits are being launched and deployed. If a spacecraft is placed on standby in cislunar orbits, whether the sensitive dynamical characteristics of the three-body problem in cislunar space can be exploited to achieve low-energy transfer to RGSO has become a key issue for expanding the utilization value of cislunar space assets.

Safe disposal of radioactive ¹³⁷Cs and ⁹⁰Sr remains a major challenge, as conventional technologies separate adsorption and solidification processes and generate excessive secondary waste. Researchers report a novel non‑hydrothermal route to fabricate robust GXU‑SODs sodalite microspheres based on geopolymer. The material achieves high‑efficiency capture of Cs⁺/Sr²⁺ and retains >96 % performance after 500 kGy irradiation. Spent microspheres realize lattice‑level permanent immobilization at 1100 ℃ without extra additives, offering a promising all‑in‑one strategy for radioactive wastewater remediation.

Researchers developed physics-embedded inverse learning (PEIL), which uses a fixed, differentiable physical solver to turn signal-recovery error into supervision for parameter estimation without parameter labels. In high-mobility wireless tests, PEIL generalized to unseen channel profiles and speeds without retraining and matched a parameter-supervised baseline using one twentieth as many training samples. PEIL was also used to estimate coil sensitivity maps for parallel MRI.

The novel physics-constrained AI framework PHICS realizes a full closed loop spanning multiscale inverse design and experimental validation. Leveraging interface, phase, and carrier engineering, PHICS breaks long-standing material trade-offs to yield composite phase-change materials that achieve simultaneous enhancements in thermal conductivity and electrical insulation while maintaining high thermal storage density, paving new pathways for designing advanced electronic thermal management materials.

Embodied AI can act in the physical world, but the purpose guiding its actions often remains buried in prompts, rewards or predefined goals. In a Review published in National Science Review, Nanning Zheng proposes Intent-Driven Embodied Artificial Intelligence (IDEAI), a four-layer framework built around explicit, revisable and verifiable intent. Seven technical pillars map how that intent could shape knowledge, planning, safety and value alignment in real-world systems.