A white-light lidar concept could measure ocean remote-sensing reflectance (R_rs) without relying on sunlight.

As high-value orbital regions become increasingly congested, pursuit–evasion confrontations among spacecraft have emerged as a critical issue in the field of space security. Active defense strategies, in which a target spacecraft releases defensive vehicles to implement counter-interception against the pursuer, constitute a three-body engagement scenario involving the target, the pursuer, and the defender. However, in actual engagements, the presence of observation noise, incomplete information, and multi-strategy adversarial conditions arising from uncertain pursuit policies render existing guidance methods—whether based on unilateral optimal control, differential games, or conventional reinforcement learning—incapable of simultaneously addressing the multiple challenges posed by unknown pursuit strategies, information deficiencies, and high-maneuverability confrontations. Consequently, how to achieve cooperative adaptive guidance between the target and the defender under conditions of incomplete observations and variable pursuit policies has become a key technological bottleneck for enhancing spacecraft survivability.

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.

Ultrasound localization microscopy (ULM) can overcome the diffraction limit but lacks generalizable imaging reconstruction and biomarker quantification pipelines. We developed U-VBA, integrating motion correction, cascaded denoising (singular value decomposition, high-pass filtering, and background subtraction), and a panel of 6 vascular biomarkers (density, intervessel distance, diameter, velocity, perfusion, and tortuosity). Performance was validated in various settings, including flow phantoms, chicken chorioallantoic membranes, rabbit optic nerve injury models, and a clinical patient cohort with cervical lymph node tumors.

This NHANES 2011–2018 cross-sectional study of 4,847 participants found that higher DXA-derived vFMI was associated with a higher prevalence of diabetes and some vascular-related outcomes. Log-transformed vFMI showed threshold patterns for diabetes and diabetic nephropathy.

The findings support further evaluation of vFMI in risk research, but they do not establish that visceral fat causes diabetes or complications and cannot replace clinical diagnosis.