In today’s society， the issue of frequent vehicle traffic accidents is still a serious practical problem. In order to ensure the trusted preservation and legal use of vehicle digital evidence， it is necessary to adopt advanced security technologies and strict access control mechanisms. Aiming at the preservation and sharing requirements of digital evidence on vehicle devices， an evidence preservation and access control scheme based on consortium blockchain was proposed. Firstly， based on consortium blockchain technology and InterPlanetary File System （IPFS）， on-chain and off-chain storage of the digital evidence was realized， while confidentiality of the evidence was guaranteed by symmetric key and integrity of the evidence was verified by hash value. Secondly， in the process of uploading， managing and downloading the digital evidence， an access control mechanism combining attributes and roles was introduced to realize fine-grained and dynamic access control management， thereby ensuring legal access and sharing of the evidence. Finally， comparison and performance analysis of the schemes were conducted. Experimental results show that the proposed scheme has confidentiality， integrity and non-repudiation with stability in the case of large number of concurrent requests.

Parallel imaging techniques can help solving problems of radiofrequency energy deposition and image inhomogeneity， reducing scan time， lowering motion artifacts， and accelerating data acquisition in ultra-high field Magnetic Resonance Imaging （MRI）. To enhance feature extraction ability to MRI complex-valued data and reduce wrap-around artifacts caused by under-sampling in parallel imaging， a Residual Complex convolution scan-specific Robust Artificial-neural-networks for K-space Interpolation （RCRAKI） was proposed. In the algorithm， the raw under-sampled MRI scan data was taken as input， and the advantages of both linear and nonlinear reconstruction methods were combined with a residual structure. In the residual connection part， convolution was used to create a linear reconstruction baseline， while multiple layers of complex convolution were utilized in the main path to compensate for baseline defects， ultimately reconstructing Magnetic Resonance （MR） images with fewer artifacts. Experiments were conducted on data acquired from a 7T ultra-high field MR device developed by the Institute of Energy of Hefei Comprehensive National Science Center， and RCRAKI was compared with residual scan-specific Robust Artificial-neural-networks for K-space Interpolation （rRAKI） under a sampling rate of 40 Automatic Calibration Signals （ACSs） and 8 speedup ratio for mouse imaging quality across different anatomical planes. Experimental results show that in sagittal plane， the proposed algorithm has the Normalized Root Mean Squared Error （NRMSE） decreased by 59.74%， the Structural SIMilarity （SSIM） increased by 0.45%， and the Peak Signal-to-Noise Ratio （PSNR） increased by 13.04%； in axial plane， the proposed algorithm has the NRMSE decreased by 7.97%， the SSIM improved slightly （by 0.005%）， and the PSNR increased by 1.09%； in coronal plane， the proposed algorithm has the NRMSE decreased by 35.03%， the PSNR increased by 5.60%， and the SSIM increased by 0.98%. It can be seen that RCRAKI performs well on all the different anatomical planes of MRI data， can reduce the influence of noise amplification at high speedup ratio， and reconstruct MR images with clearer details.

Social recommendation aims to improve recommendation effect of traditional recommendation algorithms by integrating social relations. Currently， social recommendation algorithms based on Network Embedding （NE） face two problems： one is that inconsistency between objects is not considered when constructing network， and algorithms are often restricted by positive objects that are difficult to obtain and have many constraints； the other is that the elimination of overfitting in algorithm training process based on the number of ratings cannot be realized by these algorithms. Therefore， an Adaptive Social Recommendation algorithm based on Negative Similarity （ASRNS） was proposed. Firstly， homogeneous networks with positive correlations were constructed by consistency analysis. Then， embedded vectors were obtained by combining weighted random walk with Skip-Gram algorithm. Next， similarities were calculated， and Matrix Factorization （MF） algorithm was constrained from the perspective of negative similarity. Finally， the number of ratings was mapped to the ideal rating range based on adaptive mechanism， and different penalties were imposed on bias terms of the algorithm. Experiments were conducted on FilmTrust and CiaoDVD datasets. The results show that compared with algorithms such as Collaborative User Network Embedding （CUNE） algorithm and Consistent neighbor aggregation for Recommendation （ConsisRec） algorithm， ASRNS has the Root Mean Square Error （RMSE） reduced by at least 2.60% and 5.53% respectively， and the Mean Absolute Error （MAE） reduced by at least 1.47% and 2.46% respectively. It can be seen that ASRNS can not only reduce rating prediction error effectively， but also improve over-fitting problem in algorithm training process significantly， and has good robustness for objects with different ratings.

To deal with the problem that the existing reviewer recommendation algorithms assign reviewers only through affinity score and ignore the research direction matching between reviewers and papers to be reviewed， a reviewer recommendation algorithm based on Affinity and Research Direction Coverage （ARDC） was proposed. Firstly， the order of the paper’s selection of reviewers was determined according to the frequencies of the research directions appearing in the papers and the reviewer’s paper groups. Secondly， the reviewer’s comprehensive review score to the paper to be reviewed was calculated based on the affinity score between the reviewers and the paper to be reviewed and the research direction coverage score of the reviewers to the paper to be reviewed， and the pre-assigned review team for the paper was obtained on the basis of round-robin scheduling. Finally， the final recommendation of the review team was realized based on the conflict of interest conflict inspection and resolution. Experimental results show that compared with assignment based reviewer recommendation algorithms such as Fair matching via Iterative Relaxation （FairIR） and Fair and Accurate reviewer assignment in Peer Review （PR4A）， the proposed algorithm has the average research direction coverage score increased by 38% on average at the expense of a small amount of affinity score， so that the recommendation result is more accurate and reasonable.