γ-ray astronomy has matured significantly over the course of the last decade and a half, leading to the exploration of the high-energy universe with unprecedented sensitivity. The observation of the low-energy γ-ray (0.1 to 30 MeV) sky has been significantly limited since the Imaging Compton Telescope (COMPTEL) instrument aboard the Compton Gamma Ray Observatory (CGRO) satellite was decommissioned in 2000. The exploration of γ-ray photons within this energy band, often referred to as the MeV gap, is crucial to address numerous unresolved mysteries in high-energy and multi-messenger astrophysics. Although several large MeV γ-ray missions have been proposed (e.g., e-ASTROGAM, AMEGO, and COSI), most of these are in the planning phase, with launches not expected until the next decade, at the earliest. Recently, there has been a surge in proposed CubeSat missions as cost-effective and rapidly implementable pathfinder alternatives. An MeV CubeSat dedicated to γ-ray astronomy could serve as a valuable demonstrator for large-scale future MeV payloads.

A new mapping system developed by Alliance Bioversity International and CIAT aims to tackle farm mislabeling in the coffee and cacao industries, safeguarding supply chains and consumer trust. Farm mislabeling, where the origin or type of produce is falsely claimed, has historically led to economic losses for genuine farmers and undermined fair trade practices.

The research team led by Dr. Jang SeGyu at the Functional Composite Materials Research Center of the Korea Institute of Science and Technology (KIST, President Oh Sang-rok) and the research team led by Professor Choi Siyoung at the Department of Bio and Chemical Engineering of the Korea Advanced Institute of Science and Technology (KAIST, President Lee Kwang-hyung) announced the development of a high-density BNNT protective shield. This shield, created by densely-packed BNNTs, is robust, efficiently conducts heat, and effectively blocks cosmic radiation.

Low earth orbit (LEO) mega-constellations, characterized by dense satellite coverage and low transmission latency, have substantial advantages in global communications, earth observation, and disaster forecasting. Compared to traditional constellations with simple configurations, mega-constellations have a large number of nodes and complex satellite interaction relationships and are oriented to vast global missions. As a result, when the traditional ground-based centralized management mode is applied to a mega-constellation, the ground station suffers from an explosive growth of control load and struggles to achieve the efficient, timely, and orderly operation of the mega-constellation. Therefore, it is necessary to adjust the management architecture to partially transfer the on-ground management mission to on-board management mission. Clustering satellites into multiple management domains and implementing unified management within each domain is an effective method for realizing on-board management of mega-constellations. Existing studies on the construction of satellite management domains have focused on some specific indicators, such as spatial distribution uniformity, network control latency, and controller load. Considering the highly dynamic motion of the mega-constellation, a fixed management domain construction will inevitably lead to frequent updates of the management architecture, thereby increasing the burden of on-board management. Thus, more stable strategies for constructing and updating the management domain need to be explored to reduce the frequency of management architecture updates over extended mission periods.

A machine learning framework can distinguish molecules made by biological processes from those formed through non-biological processes and could be used to analyze samples returned by current and future planetary missions.