Text-based person retrieval aims to identify specific person using textual descriptions as queries. The existing state-of-the-art methods typically design multiple alignment mechanisms to achieve correspondence among cross-modal data at both global and local levels， but they neglect the mutual influence among these mechanisms. To address this， a multi-granularity shared semantic center association mechanism was proposed to explore the promoting and inhibiting effects between global and local alignments. Firstly， a multi-granularity cross-alignment module was introduced to enhance interactions of image-sentence and local region-word， achieving multi-level alignment of the cross-modal data in a joint embedding space. Then， a shared semantic center was established and served as a learnable semantic hub， and associations among global and local features were used to enhance semantic consistency among different alignment mechanisms and promote the collaborative effect of global and local features. In the shared semantic center， the local and global cross-modal similarity relationships among image and text features were calculated， providing a complementary measure from both global and local perspectives and maximizing positive effects among multiple alignment mechanisms. Finally， experiments were carried out on CUHK-PEDES dataset. Results show that the proposed method improves the Rank-1 by 8.69 percentage points and the mean Average Precision （mAP） by 6.85 percentage points compared to the baseline method significantly. The proposed method also achieves excellent performance on ICFG-PEDES and RSTPReid datasets， significantly surpassing all the compared methods.

The dust accumulation on photovoltaic panels will reduce the conversion efficiency of photovoltaic power generation， and easily cause damage to the photovoltaic panels at the same time. Therefore， it is of great significance to recognize the dust accumulation of photovoltaic panels intelligently. Aiming at above problems， a dust accumulation degree recognition model of photovoltaic panel based on improved deep residual network was proposed. Firstly， the NeXt Residual Network （ResNeXt）50 was improved by decomposing convolution and fine-tuning down-sampling. Then， the Coordinate Attention （CA） mechanism was fused to embed the location information into channel attention， the channel relationship and long-term dependence were encoded by using the accurate location information， and the feature map was decomposed into two one-dimensional codes by using the two-dimensional global pooling operation， thereby enhencing the representation of the objects of attention. Finally， the cross-entropy loss function was replaced by the Supervised Contrast （SupCon） learning loss function to effectively improve the recognition accuracy. Experimental results show that in the recognition of the dust accumulation of photovoltaic panel at four levels of real photovoltaic power stations， the improved ResNeXt50 model has a recognition accuracy of 90.7%， which is increased by 7.2 percentage points compared with that of the original ResNeXt50. The proposed model can meet the basic requirements of intelligent operation and maintenance of photovoltaic power stations.

Security analysis of the certificateless signature scheme proposed by Guo L L, et al. (Guo L L, Lin C L, Zhang S Y. Attack and improvement for certificateless signature scheme. Computer Engineering, 2012, 38(16): 134-137,141) showed that the scheme was insecure against public key replacement attack. An improved scheme which can resist public key replacement attack was proposed. The scheme was proved to be existentially unforgeable against adaptive chosen message and identity attacks in random oracle model, and the security was reduced to computational Diffie-Hellman assumption. Compared with other certificateless signature schemes based on bilinear pairing, the improved scheme has better computational efficiency.