Xi'an Aeronautical University

Migratory birds often fly in “V” formations during long-distance migrations, a collective flight strategy that is more efficient than solo flight. Inspired by this avian behavior, researchers have validated through flight experiments the potential of trailing aircraft in formation flight to utilize the vortices generated by leading aircraft, a technique named “Surfing Aircraft Vortices for Energy (SAVE)”. This study employs computational fluid dynamics (CFD) methods to analyze the feasibility of achieving lift enhancement and drag reduction in two-aircraft formation flight through SAVE. The results demonstrate that the wingtip vortices of the leading aircraft significantly influence the aerodynamic characteristics of the trailing aircraft, with the effects depending on parameters such as aircraft size and the position of the trailing aircraft in the leading aircraft’s wake. Optimal positioning of the trailing aircraft within the vortex can increase lift/drag efficiency by approximately 10%, demonstrating the potential for energy-efficient flight in aviation. This research provides theoretical support for optimizing aircraft formation strategies and highlights the importance of biomimetic design in aerospace engineering.

This paper investigates a multi-channel access strategy for cognitive radio networks (CRNs) under bursty traffic conditions, with a focus on congestion control. The proposed approach integrates cross-layer factors including channel fading, user activity, and finite cache capacity and models heterogeneous burst service arrivals using a two-state Markov-modulated Bernoulli process (MMBP-2). A dual-threshold mechanism is implemented in the node buffer to effectively manage congestion. System states are mapped onto a two-dimensional discrete Markov chain, where state transitions are characterized by a high-dimensional transition matrix. Through steady-state analysis, key performance metrics such as average queue length, throughput, delay, and packet loss rate are derived. Simulation results confirm that the model achieves stable operational performance. Building upon this framework, this paper proposes a multi-channel access strategy that maximizes average throughput while minimizing packet loss rate by employing a genetic algorithm. The results show that, in comparison with traditional strategies, the burst flow control model developed in this study effectively meets data access requirements in highly bursty environments. Furthermore, simulation experiments explore how system performance varies with changes in the number of channels and cognitive users, and the key operational threshold is determined. These findings offer valuable guidance for channel access design and capacity planning in burst communication scenarios.

ABSTRACT Document level biomedical relation extraction aims to identify complex relationships between entity pairs in biomedical literature, which is crucial for the automation of medical knowledge applications. Existing methods face limitations when handling non‐local and multi‐layered semantic dependencies, making it difficult to effectively integrate global semantics with local interactions. The goal of this study is to propose a novel model to address this issue and enhance the ability to model complex dependencies. This paper proposes a new model that combines global dependency graphs with multi‐level semantic information graphs (DMK). By utilizing a dual‐graph collaborative mechanism, it integrates document‐level contextual information to accurately model complex dependencies between entities. We introduce the KanChebConv convolutional layer based on the Kolmogorov–Arnold Network (KAN), replacing traditional linear weight matrices with learnable spline functions, thereby enhancing the model's ability to capture non‐linear dependencies. We evaluated our model on the chemical–disease relation (CDR) dataset and the gene–disease relation (GDA) dataset. The results demonstrate that our model achieved the highest F1 score among the selected baselines on both datasets, thereby validating its robustness and competitiveness. Through the collaborative mechanism of global and local information and the innovative KAN convolutional layer, our model effectively improves the accuracy and robustness of document‐level biomedical relation extraction, showcasing strong potential for practical applications.