In view of the limitations of deep learning algorithms’ generalization and robustness， as well as the high computational cost of Full Parameter Fine-Tuning （FPFT） in object detection tasks in complex scenarios， a low-rank adaptive Parameter-Efficient Fine-Tuning （PEFT） algorithm based on YOLOv11 （You Only Look Once version 11） was proposed. Firstly， a Low-Rank Adaptation （LoRA） module was embedded into the backbone and neck networks of YOLOv11. Secondly， three low-rank decomposition algorithms， including LoRA， weight-Decomposed low-Rank Adaptation （DoRA） and Principal Singular values and Singular vectors Adaptation （PiSSA） were combined， and efficient parameter updates were achieved through weight decomposition and dynamic adjustment mechanisms. Finally， during the training process， most of the pre-trained weights of the YOLOv11 network were kept frozen， and only the low-rank matrices generated by the three low-rank decomposition algorithms in the LoRA module were trained， thereby reducing the trainable parameter size to 1.56% of the original algorithm. Experimental results on the COCO （Common Objects in COntext） dataset demonstrate that the proposed algorithm improves the precision， recall and mean Average Precision （mAP） at IoU （Intersection over Union） threshold of 0.5 by 4.18， 7.11 and 7.85 percentage points， respectively， compared with the baseline algorithm YOLOv11. It can be seen that the proposed algorithm provides an effective technical path for lightweight and efficient fine-tuning of large-scale detection algorithms in resource-constrained scenarios.

The traditional Segment Anything Model （SAM） relies on manual prompts during segmentation of Meibomian gland images， making it difficult to handle issues such as dense glands， irregular shapes， and blurred boundaries. To address this， an improved model， namely ResSAM， was proposed. ResSAM eliminated the reliance on manual intervention by introducing an automatic prompt encoder. The backbone network was pruned and optimized to further enhance the model's segmentation efficiency. Focal Loss and Smooth IoU Loss were used for training optimization， and the SE （Squeeze-and-Excitation） and cross-attention mechanisms were integrated to reduce the impact of individual differences and blurred boundaries， thereby improving the model's segmentation accuracy. Experimental results on two self-built datasets， Lower Lid and Upper Lid， showed that ResSAM achieved the best performance in terms of the number of parameters and Giga FLoating-point OPerations （GFLOPs）； its segmentation results obtained the highest Dice scores （88.69% and 87.75%， respectively） and the highest Intersection-over-Union （IoU） values （79.69% and 78.58%， respectively）. The research results indicate that the ResSAM optimizes both efficiency and accuracy， supporting early prevention and clinical diagnosis of Meibomian Gland Dysfunction （MGD）.

By analyzing the security of a certificateless signature scheme without bilinear pairing proposed by Wang Shengbao, 〖WTBX〗et al.〖WTBZ〗 (WANG S B, LIU W H, XIE Q. Certificateless signature scheme without bilinear pairings. Journal on Communications, 2012, 33(4): 93-98), it indicated that the scheme could not resist malicious attack of positive dishonest Key Generation Center (KGC). For this kind of attack, detailed attack method was given, and an improved scheme was proposed. Finally, the security of the improved scheme was analyzed. The result shows that the proved scheme can resist the malicious KGC attack, maintain efficiency of the original scheme and has higher security. Meanwhile, the communication complexity is reduced due to the elimination of the security channel.

In order to improve the Peak Signal-to-Noise Ratio (PSNR) of images, and to raise the reliability and restoration effects of the algorithm, the degradation and restoration process of image was transformed by a linear transform to be equivalent to one dimensional convolution. A constraint constant modulus cost function of blind equalization applied to medical CT images was founded, and its convexity was proved. A blind equalization algorithm based on dimension reduction was proposed. Computer simulations demonstrate the effectiveness of the algorithm. Matrix inversion is avoided to improve the efficiency of operations.