In a recent study published in Space: Science & Technology, a research team from Simon Fraser University and the University of Calgary, for the first time, adopted the brain vital signs framework to monitor cognitive and neurophysiological function in astronauts participating in a 17‑day mission to the International Space Station.

The rapid growth of electric vehicles, portable electronics, and renewable energy systems is increasing global demand for rechargeable batteries. Lithium-ion batteries currently dominate the market, but limited lithium resources and rising costs have intensified the search for affordable alternatives.

In recent years, rapidly advanced space technology has resulted in a growing number of objects in orbit, including both operational and decommissioned spacecraft, as well as space debris, which pose potential threats to China’s on-orbit spacecraft. To ensure the safe operation of spacecraft in near-Earth and high-orbit environments, a key research focus in the field of space technology is the high-resolution and high-accuracy detection and perception of environmental objects under strong backlight conditions. Such capabilities are critical for providing timely early warnings to on-orbit spacecraft. However, a plethora of studies have demonstrated marked challenges in imaging dark and weak targets under strong light backgrounds. It is essential to analyze the challenges of current backlight imaging technologies, summarize the working principles of existing backlight detection methods, and explore future development trends.

Relevant technological advancements in recent years have established solar sailing as an attractive propulsion system for terrestrial and interplanetary space missions, particularly missions that require low-thrust propulsion for long periods of time. The initial stages in designing solar sail missions involve a thorough analysis of possible transfer trajectories. Traditional approaches primarily focus on deterministic scenarios where the spacecraft dynamics is precisely defined and fully characterized. Whereas, space trajectories are inevitably subject to various sources of uncertainty, including modeling errors in the dynamics, inaccurate measurements, and unpredictable environmental effects, such as fluctuations in solar irradiance. These uncertainties necessitate the development of robust control strategies that can effectively handle stochastic effects. In recent years, reinforcement learning (RL) offers a promising framework for addressing optimization problems characterized by significant uncertainty, which can be challenging to handle using traditional methods. Meanwhile, deep reinforcement learning (DRL) utilizes neural networks (NNs) to approximate complex policies, i.e. control laws. 

As we venture deeper into space and establish bases and orbital stations on celestial bodies, it is imperative to develop sustainable space transportation methods that minimise propellant usage. Currently, the most common method for Earth-Mars transfers is through chemical propulsion systems. While the chemical propulsion system is well established, it has several drawbacks, including low efficiency, high propellant mass requirements, thermal limitations, and operational complexity. Momentum exchange tethers, which, in theory, can operate without the need for any propellant, offer an innovative alternative to these propulsion systems. Especially, in a symmetrical tether, the COM lies at the centre of the length. If both the equal mass payloads are released at the same time, then the orbit of the tether does not change. Additional delta-v can be generated by the libration (oscillation) or rotation of the tethers, which can be induced by a motor facility. Though there are various investigations into space tethers, there is a gap in the use of symmetrical tethers for interplanetary transfers. 

Sudden surface anomalies (SSAs)—ranging from earthquakes and floods to wildfires and industrial accidents—pose escalating threats to ecosystems and societies worldwide. With its advantages of wide coverage, high revisit frequency, and rich information content, remote sensing is regarded as an effective tool for rapid detection and early warning of such anomalies. However, significant technical bottlenecks remain in using remote sensing to monitor SSAs, such as delays due to revisit intervals and complex data processing chains and challenges for the integration and joint analysis of multi-source information, resulting in frequent occurrences of missed detections and false alarms. Real-time remote sensing has emerged as a transformative approach for monitoring and early warning of such abrupt events, driven by recent advances in satellite constellations, on-orbit artificial intelligence, and multi-source data fusion.

An Osaka Metropolitan University-led research team conducted a within-subject crossover study, which examined electroencephalographic source activity and heart rate variability in 29 community-dwelling elderly women during flower arrangement tasks using fresh and artificial flowers.

Lithium–oxygen batteries are widely considered a promising candidate for next-generation energy storage because of their exceptionally high theoretical energy density. However, their development has been limited by poor cycling stability, slow charging reactions, and substantial energy loss.