With the rapid advancement of global aerospace technology, human space exploration is progressively extending from near-Earth orbit to the lunar sphere. Large-scale deep-space exploration activities, such as the construction of lunar research stations and lunar bases, are gradually becoming a reality. Major spacefaring entities, including China, the United States, Europe, and Japan, have successively proposed their own lunar exploration and long-term residency programs. Current lunar exploration missions now encompass complex scenarios such as scientific investigation, in-situ resource utilization, lunar construction, and operational maintenance, which impose entirely new demands on lunar robotic technologies. However, existing lunar and Mars exploration missions still predominantly rely on pre-programmed commands and ground-based teleoperation, exhibiting limited intelligence levels. They face significant challenges in addressing five core issues: multi-task high-precision manipulation, autonomous navigation and obstacle avoidance, self-learning interactive collaboration, adaptation to extreme environments, and high-reliability long-duration operation. Consequently, the systematic integration of emerging technologies—including artificial intelligence, deep learning, large-scale models, and embodied intelligence—into lunar robotic design, so as to achieve the transition from programmed robots to intelligent robots, has become a critical technological bottleneck for supporting future large-scale lunar exploration and development.

It was not fully understood how deep-sea microorganisms maintain their structure and function under high-pressure conditions. A research team from Kyushu University, conducted an experimental study focusing on PoXeR, a protein from a deep-sea bacterium. As increasing pressure was applied to the protein, the researchers found that it assembled itself into groups of three, called a trimeric structure to stabilize itself. This adaptive strategy revealed how deep-sea proteins remain stable under extreme pressure.

Team Kinetiz takes victory at Italy’s Imola Circuit against autonomous racing teams from Germany and Italy

An international team of scientists featuring faculty at Binghamton University has recreated a 1,000-year timeline documenting the ecological changes and human impact to the area surrounding Crawford Lake, the famed location of the proposed Anthropocene. 

An international team of scientists featuring faculty at Binghamton University has recreated a 1,000-year timeline documenting the ecological changes and human impact to the area surrounding Crawford Lake, the famed location of the proposed Anthropocene. 

Accurately predicting passenger flows is essential for preventing congestion and delays in subway networks. In a recent study, researchers developed a multi-time-scale model that uses recurring day-to-day and week-to-week travel patterns to predict both passenger destinations and travel times one day ahead. Tested on Shenzhen subway data, the model outperformed several widely used forecasting methods across most stations.