In the era of 6G and dynamic spectrum access, the exponential growth of connected devices and diverse service demands intensifies spectrum scarcity and interference. Reliable spectrum prediction is thus essential to enable proactive access, alleviate congestion, and enhance spectral efficiency. Existing approaches suffer from a trade-off between accuracy and efficiency: model-driven methods often fail to capture inter-dimensional correlations, whereas data-driven methods achieve higher accuracy at the cost of excessive computational complexity. To address these challenges, we propose a lightweight spectrum prediction framework that integrates patch-based local feature extraction, sparse graph attention for efficient global dependency modeling, positional reconstruction for time–frequency alignment, and a closed-form continuous-time prediction network for accurate temporal forecasting. Simulation results demonstrate that the proposed method reduces the root mean square error by 2.7%~65% while lowering computational resource consumption by 19%~84% compared with state-of-the-art baselines. These results underline the potential of the proposed approach to support scalable spectrum management in 6G wireless networks, thereby facilitating ultra-reliable low-latency communication, massive IoT connectivity, and intelligent spectrum sharing.

Untethered capsules are capable of entering the gastrointestinal (GI) tract and collecting fluid samples containing microbial communities from specific locations, facilitating the study of chronic diseases. However, existing sampling capsules are designed for single-site sampling, making it challenging to gather samples from multiple targets. This paper reports a magnetic-driven capsule for multiple sampling within the GI tract and an on-demand magnetic-triggered fluid sampling strategy. The capsule consists of a body, a magnetic-triggered negative pressure unit, and a reservoir unit. Composed of an elastic membrane and Magnet I, the negative pressure unit controls pressure change inside the capsule cavity on demand to pump the sample by switching the magnetic field, while the embedded Magnet I also enables real-time magnetic localization for regional targeting and position tracking. The reservoir unit integrates three sampling papers for fluid absorption, two waterproof layers that maintain contamination levels below 25% to ensure reliable multi-site sampling, and a rotating arm embedded with Magnet II for posture adjustment of the sampling paper. The pumping and storage performance of the capsule was systematically evaluated and optimized. Meanwhile, the capsule, actuated by an external magnetic field, was evaluated for its active locomotion performance. Finally, the feasibility of using the capsule to perform active navigation and multi-target sampling in a porcine intestine was validated viaex vivoexperiments.

Nowadays, individuals with and without technical knowledge have started utilizing the technology to a broader extent. The utilization of technology has gone deeper concerning gadgets that aid in wireless communication with anyone or anything. This advancement has paved the way for a trending technology named wireless communication network (WCN) in recent years. In addition to this, there has been a significant change and development in the field of trading goods. Manual ordering of goods has changed to online ordering, and hence, supply chain management. This research focuses on applying WCN to a logistic tracking information system (LTIS) for a university with automatic control of the system. A novel algorithm named intelligent logistics system construction algorithm is implemented to evaluate the efficiency and performance of data. This model aids in the tracking goods and automatic control of university logistics.