Peptide sequencing using tandem mass spectrometry for proteolytically digested peptides （referred to as peptide identification） is a foundational technology in proteomics research. Current de novo peptide sequencing algorithms face challenges in identifying phosphopeptides accurately， which are of significant biological importance. The primary reason is the complex fragmentation patterns induced by phosphorylation， the frequent occurrence of neutral loss peaks， and the low abundance of phosphopeptides' mass spectrum in conventional mass spectrometric data. To address these issues， a Transformer and Gated Recurrent Unit （GRU）-based de novo sequencing algorithm for phosphopeptides was proposed， namely TGNovo. A spectrum graph was introduced in TGNovo to model the mass differences between peaks explicitly， guiding the Transformer encoder to capture spectral features. The Transformer module and the GRU module jointly model the association between spectral and amino acid sequence features and the dependencies among spectral peaks and amino acids， respectively， working in concert to achieve peptide reconstruction. Compared to the fully Transformer-based de novo sequencing algorithm Casanovo， TGNovo fully utilizes prior spectral information through the spectrum graph and GRU module， enhancing the model's ability to model spectrum graph. In evaluations of phosphopeptide fragments across species， TGNovo outperforms Casanovo with average improvements of 16.5 percentage points in peptide-level recall and 37.1 percentage points in amino acid-level recall. Additionally， experimental results on an immune peptide dataset show that TGNovo-identified high-confidence antigenic peptides cover 86% of the database search results.

Aiming at the problems of Byzantine Fault Tolerant （BFT） consensus mechanisms in the blockchain such as high communication complexity， complex view change and poor scalability， a consensus algorithm based on binomial swap forest and HotStuff named HSP （HotStuff Plus） consensus algorithm was proposed. In order to realize signature batch verification and signature aggregation， the Boneh-Lynn-Shacham （BLS） signature algorithm was adopted； in order to reduce the communication complexity of the system， threshold signature technology was adopted； in order to reduce the communication complexity during view change， the consensus process adopted a three-phase confirmation method； in order to reduce the number of communications between the primary and secondary nodes and reduce the pressure on the primary node when aggregating signatures， an improved binomial swap forest technology was adopted. Test results show that when the total number of system nodes is 64 and the request and reply are both 256 bytes， the throughput of HSP consensus algorithm is 33.8% higher than that of HotStuff consensus mechanism， and the consensus delay of HSP consensus algorithm is 16.4% lower than that of HotStuff consensus mechanism. It can be seen that HSP consensus algorithm has better performance when the number of nodes is large.

Concerning the instability of Uighur handwriting identification by texture, the authors proposed a text-independent method of handwriting identification based on feature fusion, and feature fusion involved mesh-window microstructure feature and curvature-direction feature. On the basis of extracting edge strokes from original image, a large number of local window models were created. By scanning the edge image, the probability density distribution of the feature fusion structure was obtained. And a variety of distance formulas were used to calculate the distance between the probability density feature vectors. The experimental identification rate is 89.2% in the database involving 80 handwritings. This method can portray the local writing trends of the handwritings and the curvature-direction of the strokes, the proposed method adopts probability density distribution to statistically record the mesh-window microstructure features and the curvature-direction features, and the identification effect is satisfactory.