Dynamic graph representation learning (DGRL) aims to model the temporal evolution of graph structure and attributes, thereby generating low-dimensional node representations at different time steps. Most prevailing snapshot-based methods construct snapshots independently in time, assigning each interaction to a single snapshot. However, such a design limits the ability to capture long-range temporal patterns, leading to the forgetting of prior interactions and reducing the capacity of the model to recognize causal dependencies across events. To address this issue, we construct temporal blocks with the memory replay mechanism by sequentially merging several adjacent snapshots to capture long-range temporal patterns and causal dependencies over time. Building on this, we propose a novel dynamic graph representation learning model named TBD. Specifically, the model first encodes each temporal block using a graph neural network (GNN), and then captures cross-block dynamics through a Multi-Feature Gated Recurrent Unit (MF-GRU) that incorporates structural embeddings and a feature-aware gating mechanism to adapt to evolving graph structures. Furthermore, we introduce a Structure-Aware Node Smoothness Constraint (SA-NSC) to enforce temporal consistency while retaining adaptability to structural changes. Extensive experiments on multiple real-world datasets demonstrate that TBD consistently achieves superior performance, validating its effectiveness and robustness.

In response to the increasing demand for marine data transmission, underwater acoustic (UWA) communication is widely recognized as a key technology to support various applications of the Internet of Underwater Things (IoUT). However, due to the narrow bandwidth shared by multiple acoustic systems and the constrained energy of the underwater nodes, it is an urgent problem to design an energy-efficient resource allocation scheme which can avoid interference simultaneously. In this article, we establish a cognitive orthogonal frequency-division multiple access (OFDMA) UWA communication system, where dynamic resource allocation relies on the feedback channel state information (CSI) and spectrum usage of interference. In order to detect interference, a spectrum sensing scheme applicable to the underwater environment is proposed and experimentally verified. Subsequently, the resource allocation problem is mathematically modeled with energy efficiency (EE) as the reasonable optimization objective in UWA communication systems. In order to reduce the computational complexity, the original problem is decomposed into two subproblems and a low-complexity algorithm named acrlong BOSC- acrlong TLI is developed for feasible solutions. Simulation and sea experiment results show that the proposed algorithm outperforms the existing underwater resource allocation schemes.

Coastal grounding electrodes are currently an important means to alleviate land grounding electrode land constraints. In order to better invert the terrestrial geodesic resistivity in the coastal region, this paper proposes a complete set of inversion technology schemes. First, this paper proposes a layered land model for the coastal region, and a composite geodetic model is modeled by the fold junction of the land model and the ocean. Based on this, an adaptive subdivision boundary element method is proposed for solving the composite soil grounding calculation problem, and the accuracy and advantages of the method are demonstrated by examples. Finally, the paper uses the differential evolutionary algorithm to invert the exploration data of the four-point method in the coastal area, and obtains the parameters of the terrestrial layered geodetic model that meet the engineering requirements. The comparison with the grounding software CDEGS illustrates the effectiveness of the method. This paper carries out the research on the modeling and inversion methods of composite layered soil model, combining advanced numerical calculation methods and artificial intelligence algorithms to provide the support of computational tools for coastal resistivity inversion.