A team of AI scientists and seismologists has developed a pioneering self-supervised framework, DASFormer, for high-resolution earthquake monitoring. Built on a two-stage, coarse-to-fine architecture, DASFormer is pre-trained on vast amounts of unlabeled Distributed Acoustic Sensing (DAS) data collected from existing fiber-optic cables (e.g., Internet cables). Acting like a “self-taught seismologist”, the model first learns the predictable patterns of background noise and then flags earthquake signals as anomalies that defy its forecasts. This novel approach demonstrates superior performance, outperforming other state-of-the-art models. Its versatility extends to challenging environments such as the seafloor, underscoring its potential for scalable, automated seismic intelligence.

The civilian GPS signals are vulnerable to spoofing attacks in UAV system. A new on-board algorithm named MSSTP-OAD enables low-cost drones to detect counterfeit GPS positions in real time using only their existing GPS receiver and inertial unit—no extra radios, antennas or ground stations required. Compared to existing LSTM-based algorithms, the proposed method can achieve 98.4 % accuracy, 24 % faster detection and 26 % shorter recovery distance.

Flexible composites-based piezoelectric nanogenerator (PENG) with low cost, stable properties and sensitivity to mechanical deformation is highly suitable to construct self-powered sensing layer for distributed electrical transmission power lines, and this innovation can help reduce manual maintenance costs. However, the lower output performance of the PENG hinders its integration with energy management circuits and signal recognition systems. In this study, a high-performance PENG was achieved by designing branch-heterostructure piezoelectric ceramic fibers, which can enhance the charge transport mechanisms and induced polarization. Moreover, an intelligent Power Internet of Things system through the synergistic integration of this high-performance PENG and learning-assisted data analytics has been constructed, which enables accurate self-powered real-time monitoring of abnormal vibration states in transmission power lines with approximately 96% identification accuracy. This work not only provides an effective strategy to enhance PENG performance, but also offers a solution to improve the reliability of power grid operations and optimize maintenance efficiency. Recently, a team of material scientists led by Haowei Lu from Henan University, China, prepared high-performance PENG based on a new piezoelectric ceramic fiber, which is beneficial to the improvement of electrical output performance by enhanced induced polarization and directed charge transport mechanism. Moreover, based on this PENG, a power grid transmission line vibration determination system with high identification accuracy can be constructed in this study.

Unmanned aerial vehicles (UAVs), characterized by their low cost and operational flexibility, can be utilized to realize the comprehensive spatial coverage for the sixth-generation mobile networks. However, the private data in UAV networks is easy to be exposed due to the line-of-sight links and openness of wireless transmission. Covert communication as an emerging technique has shown its superiority in hiding the transmission behavior to further enhance the security of UAV networks. Therefore, we present a survey on the recent advanced research about covert UAV communications.

The gut microbiota is widely recognized as a central regulator of human health and disease. Medicine-food homologous resources, leveraging their inherent safety and multi-target characteristics, serve as pivotal modulators for intervening in metabolic, inflammatory, and immune-related disorders via microbiota regulation. However, the inherent complexity, substantial interindividual variability, and dynamic nature of the gut microbiome remain major hurdles to achieving precise interventions. This perspective delineates a novel paradigm for precision medicine-food intervention, built upon three interconnected and cutting-edge directions: (1) targeting key microbial metabolites, (2) advancing targeted delivery technologies for beneficial microbes, and (3) implementing artificial intelligence (AI)-assisted personalized microbiome functional profiling. This triad synergistically addresses the challenge of individual variability and paves the way for highly effective and precise interventions.

UAV shipboard landing has gained extensive attention, due to its potential to enhance operational efficiency in maritime applications, including surveillance, inspection, refueling, and sea rescue missions. However, the oscillatory ship motion caused by the sea wave interactions and wind gusts, especially in rough sea states, may lead to UAV trajectory deviations or even collisions, significantly escalating the challenge of shipboard landing. Consequently, the development of safe and reliable UAV shipboard landing techniques is of great importance and remains a critical research priority.

Slurries with high solid loading and low apparent viscosity are critical for spontaneous coagulation casting (SCC) and other in situ slurry solidification techniques. When the solid loading of slurry is increased, it helps to reduce the drying shrinkage of wet body, the sintering shrinkage of green body and accelerate the densification process of ceramics. In recent years, many scholars have dedicated to increasing the solid loading of the alumina ceramic slurry. However, there is no breakthrough about an alumina slurry with both high solid loading and low apparent viscosity. An excessively high apparent viscosity will make it difficult to debubble, thereby reducing the density of the green body.

Antimicrobial resistance has become one of the top global public health and development threats due to the misuse and overuse of antimicrobials in humans, animals, and plants. Researchers are leveraging artificial intelligence and interdisciplinary approaches to design antimicrobial peptides (AMPs) that show a reduced risk of inducing resistance. Precise targeting design makes AMPs more efficient for combating drug-resistant bacteria and fungi, with applications spanning medicine, agriculture, and food safety.

This article discusses the transformative role of spatial metabolomics in advancing research on "food-medicine homology." By integrating metabolomics with spatial analysis technologies, this approach preserves the original spatial distribution information of metabolites within tissues, enabling a paradigm shift from mere component identification to precise localization. The paper highlights that food-medicine homology substances exhibit multi-component synergies, spatiotemporal dynamics, and strong environmental dependencies. Spatial metabolomics allows visual tracking of the absorption, distribution, and metabolic pathways of these components in vivo, reveals interaction mechanisms among components, gut microbiota, and the host, and provides chemical evidence for evaluating the geo-authenticity of medicinal materials. Despite challenges such as high detection costs and a lack of technical standardization, spatial metabolomics is poised to transition food-medicine research from macroscopic effect evaluation to microscopic spatial resolution. It holds promise for supporting personalized dietary recommendations, intelligent cultivation technologies, and the modernization of traditional medicine, ultimately contributing to global health innovation under initiatives like "Healthy China 2030."