Sleep apnea affects quality of life and health seriously. PolySomnoGraphy （PSG） is the “gold standard” for diagnosis of sleep apnea， but it is expensive and inconvenient for long-term monitoring. Based on the above， a new method based on universal smart wristband was proposed to detect sleep apnea conveniently. In the method， by analyzing heart rate， blood oxygen saturation， and sleep state data collected by the wristband， an adaptive physiological data reconstruction method and a data interpolation method were used to achieve noise filtering； in feature engineering， continuous physiological variables and categorical variables were fused to extract sleep state features deeply； in the classification module， a lightweight Gated Recurrent Unit （GRU） model was used to simplify the training process and reduce the risk of overfitting. Experimental results show that the proposed method obtains 93.68% accuracy and 93.97% recall on a 23-person dataset. Correlation analysis shows that blood oxygen saturation， body mass index， and age are confirmed as key features for sleep apnea detection. Compared with PSG， the proposed method is more suitable for long-term monitoring in a home environment.

There are limited memory and computing power of the equipment in smart construction sites， making it very difficult to detect rebar in real time through object detection on the on-site equipment. The slow speed of rebar detection and the high cost of model deployment of this equipment also bring great challenges. In order to solve the problems， RebarNet， a lightweight network for rebar detection with attention mechanism was proposed on the basis of YOLOv3 （You Only Look Once version 3）. Firstly， the residual block was used as the basic unit of the network to construct a feature extraction structure to extract local and contextual information. Secondly， Channel Attention （CA） module and Spatial Attention （SA） module were added to the residual block to adjust the attention weight of the feature map and improve the ability of the network to extract features. Thirdly， the feature pyramid fusion module was used to increase the receptive field of the network and optimize the extraction effect of the medium-sized rebar images. Finally， the feature map of 52×52 channel was output for post-processing and rebar detection after 8 times downsampling. Experimental results show that the parameter amount of the proposed network is only 5% of that of Darknet53 network， and mAP （mean Average Precision） of the proposed network achieves 92.7% at the speed of 106.8 FPS （Frames Per Second） on the rebar test dataset. Compared with the existing 8 object detection networks including EfficientDet （Scalable and Efficient Object Detection）， SSD （Single Shot MultiBox Detector）， CenterNet， RetinaNet， Faster RCNN （Faster Region-CNN）， YOLOv3， YOLOv4 and YOLOv5m （YOLOv5 medium）， RebarNet has a shorter training time （24.5 seconds）， the lowest memory usage （1 956 MB）， and the smallest model weight file （13 MB）. Compared with the current best-performing YOLOv5m network， RebarNet has the mAP slightly lower by 0.4 percentage points with the detection speed increased by 48 FPS， which is 1.8 times of that of YOLOv5m network. The above indicates that the proposed network helps to complete the task of high-efficiency and accurate rebar detection in smart construction sites.

By discussing the shortages of access control policy, and analyzing the complementarity and completeness between access control and state control, the necessity of state control was proposed. A formal description about state control policy was defined, and the policy's description rules based on XML were regulated. At the same time, according to different control goal and control object, some application patterns for state control policy were provided. In addition, the complexity of state control policy was discussed, and some solutions were provided.

The braid group is a new candidate platform for constructing quantum attack-resistant public key encryption system. The proxy signature allows a proxy signer to sign messages on behalf of an original signer who delegates his signing power to the proxy signer. A proxy signature based on root finding problem over braid group was put forward, the analysis of its correctness, security, efficiency and parameter choice were subsequently presented, and afterwards the detailed formal security proofs in the Random Oracle Model (ROM) were given. The new scheme can resist current known attacks.