Compared with botanical medicines, the intelligent identification of animal-derived medicines has developed relatively slowly and presents greater challenges due to species diversity, substantial morphological variations after processing, and the prevalence of adulteration. Tortoise plastron is a representative animal-derived medicine whose subtle morphological differences complicate reliable authentication. This study aimed to establish a standardized image-based framework to achieve accurate and reproducible identification of tortoise plastron and its common adulterants. An RGB image dataset covering Chinemys reevesii , Mauremys mutica , Ocadia sinensis , Malayemys subtrijuga , and Trachemys scripta elegans was constructed. Images were enhanced through geometric transformations, color jittering, and Gaussian blurring to simulate diverse acquisition conditions. Segmentation was performed using the SAM2 model to remove background noise and extract core regions, and classification was conducted using YOLO11 models of three scales (m, l, x). Training and validation were carried out on datasets with and without segmentation-based augmentation, each repeated three times, with average performance recorded. Statistical analysis included comparison of model performance metrics across datasets and model scales. The combined preprocessing–segmentation–classification workflow effectively captured both global and fine-grained features of tortoise plastron. YOLO11m with segmentation-based augmentation achieved the most balanced performance across accuracy and robustness. Eight technical modules, including attention-enhanced feature extraction and multi-scale pooling, contributed to improved classification precision. A practical recognition application was developed to facilitate user-friendly deployment. This study established a comprehensive digital framework for the objective identification of tortoise plastron and adulterants, transforming subjective trait-based evaluation into quantitative image analysis. The integration of advanced segmentation and multi-scale feature fusion provides a transferable paradigm for the intelligent identification of animal-derived medicines, with potential to enhance quality control and authenticity assurance in traditional Chinese medicine.

Although Federated Learning (FL) can enable advanced autonomous driving via leveraging massive distributed data in vehicular networks, vehicle mobility causes frequent connection interruptions, hindering the FL process. In this paper, we propose a novelMobility-AwareVehicularFL(MAVFL) scheme, which can accelerate the training process in dynamic vehicular networks via adaptive vehicle selection and sparse training. Specifically, the MAVFL dynamically selects participating vehicles based on their locations and training loss. By incorporating adaptive model sparsification, the proposed scheme dynamically proceeds with sparse masks during vehicle local training, thereby reducing communication overhead while preserving model accuracy. We conduct a rigorous convergence analysis to uncover how vehicle mobility and model sparsification affect convergence rate. Furthermore, we formulate an optimization problem to accelerate the training process, which jointly optimizes vehicle selection, sparsification ratio, and bandwidth allocation to minimize training delay. To solve the problem, we employ the Lyapunov optimization method to decouple the long-term problem into a series of instantaneous subproblems. Next, a generalized Benders decomposition method structures the original problem into a master subproblem for vehicle selection and a primal subproblem for bandwidth allocation and sparsification ratio selection. The optimal solutions are derived via alternating iterations between these problems. Extensive simulation results based on the SUMO simulator demonstrate that the MAVFL accelerates model convergence by up to 14% and reduces communication overhead by up to 26% while preserving model accuracy, as compared to the state-of-the-art benchmarks.

This paper tackles the distributed computing problem in a joint cellular and device-to-device (D2D) network, where edge users can exchange information both through an access point (e.g., base station) and D2D communication links. The D2D network consists of multiple disjoint clusters, where multiple users can transmit signals simultaneously to their neighbouring users within the same cluster, due to disjoint connections between clusters. A nested coded distributed computation (CDC) scheme is proposed to reduce the communication load both in the uplink and downlink communication, by leveraging coding techniques and parallel transmission opportunities in the D2D network. It is shown that this scheme is optimal in the sense that it achieves the minimum communication load both in the uplink and downlink. The nested CDC scheme can also reduce the coding complexity, compared with the conventional wireless CDC scheme proposed by Li et al., whose computational complexity grows exponentially with the computation load.