The integration and sharing of chemical-biological data have become increasingly crucial in advancing research in drug discovery, materials science, and catalysis. However, several key challenges persist, including ensuring accurate semantic alignment, protecting data privacy, and providing reliable decision support. Addressing these challenges is essential to improving the efficiency and security of data-sharing and analysis processes. This paper proposes a comprehensive solution that leverages deep learning for semantic alignment and blockchain technology for secure data sharing. Our platform utilizes natural language processing (NLP) and graph neural networks (GNN) to align heterogeneous chemical-biological datasets, ensuring consistency and completeness across different sources. Additionally, blockchain technology is employed to establish a decentralized and tamper-resistant data-sharing framework, enhancing security and trust among stakeholders. Through extensive experimentation using the Open Catalyst dataset, our results demonstrate the effectiveness of the proposed approach in achieving high-precision data alignment, secure data transactions, and reliable decision support. This work presents an innovative and integrated platform that addresses long-standing challenges in chemical-biological data integration and sharing, paving the way for more efficient and secure collaborative research.

In this paper, an eight-coil wireless power transfer (WPT) system is proposed to improve the power transfer efficiency. This WPT system is designed with an array of multiple coils and resonators. Even in the case of misalignment, at least one coil pair maintains effective coupling, thus ensuring the operational efficiency of the system. The proposed eight-coil design yields a consistent magnetic field distribution, which strengthens the inter-coil coupling and enhances the system’s robustness against misalignment. This paper analyzes the basic principles of the equivalent circuit diagrams of the WPT systems with two-, four- and eight-coils, and analyzes the corresponding calculation formulas such as coupling coefficient and transmission efficiency. The eight-coil WPT system’s performance has been validated through simulation software CST, which can maintain high efficiency under a certain range of misalignment. Additionally, the proposed WPT system is benchmarked against other misaligned WPT systems to highlight its advantages.

To ensure the safety of both aircraft and operators in low-altitude economy (LAE) activities, precise environmental perception capabilities are essential for effective collision prevention. However, achieving accurate perception remains challenging, particularly when obstacles are camouflaged and visually blended into their surroundings, making detection difficult, even with the robust foundation model, the Segment Anything Model (SAM). Although SAM's prompt-based strategy improves its performance in the Camouflaged Object Detection (COD) task, its reliance on limited prompts introduces new challenges. Instead of manually annotating prompts, our work introduces a multimodal learning approach that utilizes a Vision-Language Model (VLM) to automatically generate mask prompts. By integrating visual and textual information, this work generates high-quality prompts that significantly enhance the performance of SAM in identifying camouflaged objects. Experimental results demonstrate that the proposed method achieves an average improvement of 13% over the baseline SAM across three COD benchmark datasets.

With the concept of low-altitude economy (LAE) being released recently, the research on ultra-low latency communication and rich computation capacity technologies to support LAE-enabled internet of things (IoT) networks has attracted interest from industry and academia. One of the key challenges is to reduce the latency of the IoT networks while guaranteeing the quality of service among all user devices (UDs). In this paper, we propose an LAE-enabled IoT network, where a UAV-carried mobile edge computing (MEC) server offers extra computation capacity to all UDs to process their computational tasks remotely. To minimise the total service delay of all UDs, which consists of the transmission delay, processing delay, queueing delay, and UAV mobility delay, we propose a deep-Q-leaning (DQN)-based optimisation algorithm by jointly optimising the task offloading decisions and communication and computation resource allocation for all the UDs in the LAE-enabled IoT network. Simulation results illustrate that our proposed algorithm achieves a much lower total service delay than the benchmarks.

With the widespread application of Unmanned Aerial Vehicles (UAVs) in domains like military reconnaissance, emergency rescue, and logistics delivery, efficiently planning the shortest flight path has become a critical challenge. Traditional heuristic-based methods often suffer from the inability to escape from local optima, which limits their effectiveness in finding the shortest path. To address these issues, a novel Improved Grey Wolf Optimizer (IGWO) is presented in this study. The proposed IGWO incorporates an Advanced Cooperative Predation (ACP) and a Lens Opposition-based Learning Strategy (LOBL) in order to improve the optimization capability of the method. Simulation results show that IGWO ranks first in optimization performance on benchmark functions F1–F5, F7, and F9–F12, outperforming all other compared algorithms. Subsequently, IGWO is applied to UAV shortest path planning in various obstacle-laden environments. Simulation results show that the paths planned by IGWO are, on average, shorter than those planned by GWO, PSO, and WOA by 1.70m, 1.68m, and 2.00m, respectively, across four different maps.

Semantic segmentation confronts challenges with traditional networks tailored exclusively for RGB inputs, which may suffer from quality degradation under adverse conditions like low-level illumination or inclement weather. Recent advancements have shown promising outcomes by integrating RGB images with corresponding thermal infrared (TIR) images. However, effectively fusing features from both modalities remains a significant challenge. In this paper, we introduce a novel approach termed Multimodal Frequency Spectrum Fusion Schema (MFSFS) for semantic segmentation of RGB-T images. MFSFS leverages the advantages of the frequency spectrum to effectively extract and utilize multimodal feature information. To mitigate redundant information’s adverse effects during multimodal fusion in the frequency domain, we propose a diversity-oriented contrastive learning approach. Simulation results demonstrate that MFSFS achieves competitive performance while maintaining a relatively smaller model size.

With the development of Fifth Generation (5G)/Sixth Generation (6G) -enabled Internet of Things (IoT) networks, different user equipment (UE) dynamically generates massive raw data and delay-sensitive computation tasks to be offloaded and processed at the mobile edge computing (MEC) nodes. In this paper, we propose a comprehensive digital twin-empowered UAV-assisted edge intelligent IoT framework, which enables UEs to offload their delay-sensitive tasks to a UAV-assisted MEC node. We aim to minimise the maximum total service delay including the transmission delay and the processing delay among all UEs. A deep deterministic policy gradient-based offloading and resource allocation optimisation algorithm, named (DDPG-ORAO), is proposed to optimise task offloading decisions among all UEs, which jointly optimising the communication and computation resources allocation among all UEs and all UAV-assisted MEC nodes. Simulation results show that our proposed optimisation algorithm outperforms the benchmarks in terms of the total service delay of all UEs.

Vehicular communications in future sixth-generation (6G) networks are expected to leverage integrated sensing and communications (ISACs) and mobile edge computing (MEC) techniques. However, the rapid proliferation of vehicle user equipment (V-UE) and the diversity of ISAC-aided and MEC-empowered vehicular communication and computation services demand a more intelligent and efficient resource allocation framework for the next-generation vehicular networks. To address this issue, we propose a comprehensive ISAC-aided vehicle-to-everything (V2X) MEC framework, where the V-UEs can offload their tasks to the edge server collocated at the roadside unit (RSU). We aim to minimize the long-term average total service delay of all the V-UEs by jointly optimizing the offloading decisions of all the V-UEs, the computation resource allocation at the ISAC-aided RSU, the transmission power, and the allocation of resource blocks for all the V-UEs, where the total service delay of a V-UE includes the task processing delay and the transmission delay if the V-UE offloads its task to the RSU. To solve the formulated mixed integer nonlinear programming problem, we design a multiagent deep deterministic policy gradient (MADDPG)-based offloading optimization and resource allocation algorithm (MADDPG-O2RA2). Simulation results demonstrate that our proposed algorithm outperforms the benchmarks in terms of convergence and the long-term average delay among all the V-UEs.

This article presents an innovative belt antenna with an electromagnetic band-gap (EBG) ground plane made of textile materials. The antenna can be applied in a smart belt system to set up a communication link with other electronic devices and/or host a variety of sensors to track human motions. The proposed belt antenna works at 2.45 GHz in the industrial, scientific, and medical radio band for Bluetooth low energy communications. Considering the effect the human body would have on the performance of a belt antenna, a textile ground plane is designed to be integrated into the trouser fabric behind the belt to provide isolation from the body and simultaneously improve antenna radiation characteristics. Through the application of the ground plane, the belt antenna achieves a maximum realized gain of 7.94 dBi and a minimum specific absorption rate of 0.04 W/kg at 0.5 W input power. During the design process, characteristic mode analysis is used to explore the underlining principle and further optimize the antenna performance. Two typical EBG structures are analyzed in detail for this application scenario. The suspended transmission line method is used to evaluate EBG performance variations when the textile ground plane is bent. A prototype of such a system is fabricated and tested. Experimental results shows that the belt antenna, together with the textile EBG ground plane, is an excellent candidate for a smart belt system with desirable radiation pattern, efficiency, and safety limit.