With the increasingly urgent demand for non-toxic, high-specific-impulse propellants in space propulsion systems, ammonium dinitramide (ADN)-based liquid propellants have attracted extensive attention as a new-generation green alternative to hydrazine-based fuels. However, existing ADN-based thrusters all adopt the catalytic ignition technology route, which suffers from inherent drawbacks: the catalyst cannot withstand temperatures exceeding 1500 K, long preheating times are required prior to startup (in the Prisma mission, in-flight preheating lasted 600–720 s with a single-event energy consumption of 25 kJ), and insufficient preheating may lead to "hard start" or even explosion—these issues severely constrain the rapid response capability and operational safety of the thrusters. Although active ignition methods such as resistive ignition and laser ignition have been validated at the single-droplet level, how to achieve catalyst-free electric ignition at the thruster system level and systematically evaluate its combustion characteristics remains a critical engineering problem urgently to be addressed in the field of green space propulsion.

Plasma bubbles in the ionosphere over equatorial and low-latitude regions are a common nighttime plasma density depletion structure that can cause severe scintillation interference on high-frequency communications, making them an important target of space weather monitoring. Traditional detection methods rely on statistical thresholds derived from temporal fluctuations in ion density; however, plasma bubbles exhibit significant variability in spatiotemporal scales (ranging from kilometers to hundreds of kilometers), and their occurrence frequency is strongly modulated by solar activity and seasonal factors. Consequently, fixed-threshold methods struggle to accommodate this diversity, with miss rates reaching up to 40% during periods of low solar activity. Although deep learning has demonstrated powerful capabilities in automatically extracting and recognizing complex waveform features in fields such as medical image segmentation and signal peak detection, its systematic application to ionospheric plasma bubble detection—and thus addressing the adaptability deficiencies of traditional threshold methods—remains underexplored.

With the long-term on-orbit operation of space stations, extravehicular status monitoring and surface inspection tasks have imposed higher demands on the motion planning capabilities of manipulators. Space redundant manipulators, when performing visual monitoring tasks, must simultaneously satisfy multiple complex constraints including kinematics, dynamics, obstacle avoidance, and end-effector line-of-sight, making this a typical nonlinear non-convex optimal control problem. Although sampling-based methods can rapidly generate collision-free paths, they struggle to guarantee dynamic feasibility in high-dimensional systems and often yield poor-quality paths. Optimization-based methods, while capable of directly generating high-quality trajectories, are highly sensitive to initial solutions and lack effective warm-start mechanisms. Therefore, how to merge the rapid search capability of sampling-based methods with the precise solving advantage of optimization-based methods to plan high-quality executable trajectories under complex constraints for space manipulators has become a key challenge in advancing the intelligent development of space robotics.

A 178-year-old mystery surrounding one of the earliest recorded examples of space weather affecting technology has been solved by an international team led by Lancaster University.

The first practical electric telegraph networks were deployed during the 1840s, and as solar activity increased during the following years, Victorian telegraph operators began to experience unexplained electrical effects caused by geomagnetic disturbances.

Professor Jim Wild from Lancaster University with scientists from the British Geological Survey, Natural Resources Canada, Baylor University, RMIT University, and STFC RAL Space, re-examined a widely cited account of a train delay in Exeter that was reportedly caused by geomagnetic disturbance from the sun interfering with railway’s telegraph systems.

The event has previously been cited as potentially the earliest recorded example of space weather affecting human technology but the team’s research published in the American Geophysical Union journal Space Weather contradicts this assumption.

Astronomers have confirmed that one of the fastest-changing stars ever observed has entered a new stage of its evolution, offering a rare opportunity to watch a star's life unfold on human timescales.

Researchers have developed a multimodal artificial intelligence framework that predicts the research octane number (RON) of both pure fuel components and complex mixtures with high accuracy. For pure components, the model achieved an R² of 0.9373 and a mean absolute error (MAE) of 4.04. For mixtures, the latent-space model achieved an R² of 0.9736 and an MAE of 1.46—reducing prediction error by 69.8 % compared with conventional linear blending rules. The model also enables inverse fuel formulation design, allowing researchers to search for feasible component proportions that meet target RON constraints.