Xi Hu

This simulation-based exploratory study examines HGPC-CSA-PSO, a hybrid biomimetic optimization framework integrating the Giza Pyramids Construction Algorithm (GPC), Crow Search Algorithm (CSA), and Particle Swarm Optimization (PSO) for dynamic collaborative grouping in interpreting education. The educational scenario and initialization values are modeling assumptions; no classroom intervention, causal teaching experiment, or independently auditable empirical validation is reported. The scalar proficiency index Q_i is used as an education-oriented evaluation-layer measure, whereas the checked-in algorithms optimize their original unweighted coordinate-mean fitness. An illustrative educational-model trajectory reaches its stated scalar threshold after 29 simulated interaction updates. Separately, under the standardized repository configuration, 30 paired runs use the implementation-level termination rule that every coordinate of every simulated student must reach 0.99. Under that configuration, HGPC-CSA-PSO converges in 16.367 ± 1.974 updates and is faster than GPC and CSA (Holm-adjusted p < 0.001 for both), but not significantly different from PSO (Holm-adjusted p = 0.194). These two result layers are not interchangeable and provide model-level computational evidence only.

Printed circuit boards (PCBs) defect detection constitutes a critical stage in electronic manufacturing, serving as a vital safeguard for product reliability, functional safety, and overall quality in industries such as automotive, aerospace, and consumer electronics. However, existing methods still face some key limitations, including the frequent omission of micron-scale defects caused by insufficient receptive fields and coarse feature representations, alongside high computational costs that hinder real-time deployment on resource-constrained production equipment. Therefore, this paper proposes You Only Look Once (YOLO)-MDMN—an enhanced variant of YOLOv12n. First, the improved Monte Carlo receptive field module is integrated into the A2C2f module, which utilizes stochastic multi-scale sampling to reduce both target fragmentation and computational overhead. Second, the dynamic spatial-preserving feature neck is utilized to combine Space-to-Depth encoding with content-aware upsampling and adaptive fusion to preserve high-frequency details during scaling. Third, the Detect_MBConv detection head incorporates adaptive expansion ratios, scale-aware depthwise convolution, and dual-pathway attention for improved efficiency. Finally, the Shape-Nwd loss function is applied to combine geometric constraints with adaptive Gaussian modeling for stable micro-defect localization. Experiments on PKU-Market-PCB dataset show YOLO-MDMN achieves 93.8% mAP50, outperforming YOLOv12n by 3.5%, while reducing parameters by 39.7% and computation by 40.6% and achieving real-time inference at 344.8 FPS, which demonstrates efficacy for resource-constrained industrial deployment.