Span-based joint extraction model shares the semantic representation of entity spans in entity and Relation Extraction （RE） tasks， which effectively reduces the cascade error caused by pipeline models. However， the existing models cannot adequately integrate contextual information into the representation of entities and relations. To solve this problem， a Joint Entity and Relation extraction model based on Contextual semantic Enhancement （JERCE） was proposed. Firstly， the semantic feature representations of sentence-level text and inter-entity text were obtained by contrastive learning method. Then， the representations were added into the representations of entity and relation to predict entities and relations jointly. Finally， the loss values of the two tasks were adjusted dynamically to optimize the overall performance of the joint model. In experiments on public datasets CoNLL04， ADE and ACE05， compared with Trigger-sense Memory Flow framework （TriMF）， the proposed JERCE model has the F1 scores of entity recognition improved by 1.04， 0.13 and 2.12 percentage points respectively， and the F1 scores of RE increased by 1.19， 1.14 and 0.44 percentage points respectively. Experimental results show that the JERCE model can fully obtain semantic information in context.

Aiming at the limited scalability of medical data sharing based on traditional blockchains， a scale-out and sharing scheme of blockchain based on sharding technology was proposed. Firstly， the periodic network sharding was performed based on the jump consistent hash algorithm， and the risk of Sybil attacks in a single shard was greatly reduced by randomly dividing the network nodes. Then， the Scalable decentralized Trust inFrastructure for Blockchains （SBFT） consensus protocol was used in the shards to reduce the high communication complexity of the Pratic Byzantic Fault Torent （PBFT） consensus protocol， and the two-layer architecture was used between the physical multi-chain of shards and the logical single chain of the main chain to reduce the storage pressure of the members of shards. Finally， a multi-keyword association retrieval searchable encryption sharing scheme based on Public key Encryption with Conjunctive field Keyword Search （PECKS） was proposed on the medical consortium blockchain， so as to improve the patients’ control over their sensitive data， and realize the fine-grained search of sensitive data under encryption. Through performance analysis， it can be seen that under the parallel sharding structure， the throughput of blockchain is significantly increased with the increase of shards， and the retrieval efficiency is also significantly improved. Experimental results show that the proposed scheme can greatly improve the efficiency and scalability of the blockchain system.

To resolve the peculiar problem of the Jacobian matrix for a special class of nonlinear equations, an improved Newton mtheod was proposed based on the dual space. This paper proposed an explicit formula to compute the dual space of an ideal in a point through polynomial multiplication, and constructed augmented equations using the dual space. Meanwhile, the Jacobian matrix of augmented equations at initial point was full rank, and then the algorithm recovered quadratical convergence of Newton's iteration. The experimental results show that after three iterations, the accuracy of computation can achieve 10^(-15). The proposed method further enriches the theories of the dual space of ideal in algebra geometry and provides a new method for the numerical calculation in engineering applications.

As the unsteady bandwidth of mobile channel and the limited coverage of wireless Wi-Fi point, a wireless video transmission system based on adaptive choice of CDMA1X, 3G-EVDO and Wireless Local Area Network (WLAN) was proposed. The adaptive choice algorithm includs: adaptive access of multiple networks, dynamic switch of wireless heterogeneous networks and adaptive choice of homogeneous networks. The presented method can achieve high quality and reliable transmission.