In multivariate long-term time series forecasting， only relying on time domain analysis often falls to capture long time-series dependencies， leading to insufficient information utilization and not high enough prediction accuracy. To solve these problems， combined with time and frequency domain analyses， a Frequency-Sensitive Dual-branch Transformer with Discrete Fourier Transform （DFT） for multivariate long-term series forecasting （FSDformer） method was proposed. Firstly， by utilizing DFT， the transformation between time and frequency was accomplished， allowing the decomposition of complex time-series data into three structurally simple components： low-frequency trend item， medium-frequency seasonal item， and high-frequency residual item. Then， a dual-branch structure was adopted： one branch dedicated to predict medium- and high-frequency components， with an Encoder-Decoder structure applied to design a periodic enhancement attention mechanism， and another dedicated forecast to low-frequency trend components， with a MultiLayer Perceptron （MLP） structure. Finally， the prediction results from both branches were aggregated to obtain the final multivariate long-term time series forecasting results. FSDformer was compared with five classical algorithms on two datasets. On the Electricity dataset， when the historical sequence length is 96 and the predicted sequence length is 336， compared to the comparison algorithms such as Autoformer， FSDformer decreases the Mean Absolute Error （MAE） by 11.5%-29.1%， and decreases the Mean Square Error （MSE） by 20.9%-43.7%， reaching the optimal prediction accuracy. Experimental results show that， FSDformer can capture the dependencies within long-term time series data efficiently， and can improve the prediction stability of model while enhancing prediction accuracy and computational efficiency.

In view of the limited accuracy of imaging algorithm，the nonlinear Contrast Source Inversion (CSI) algorithm combined with regularization and Concurrent Frequency (CF) was proposed for reconstructing a hybrid target in an anechoic chamber. The experimental data were obtained using multi-frequency multi-bistatic measurements. The reconstructed position, shape and contrast value of the target were presented, verifying the accuracy of the extended CSI algorithm for reconstructing the complicated 2-D hybrid targets.

Smart contracts are the core component of blockchain technology. With the rapid popularity of blockchain applications at home and abroad， security incidents caused by smart contract vulnerabilities occur frequently， resulting in huge economic losses. In response to the above problems， smart contract vulnerability detection solutions suitable for different scenarios have been developed based on a variety of theories and technologies. In order to understand smart contract security vulnerability detection technology systematically， the research literature related to smart contract security vulnerability detection technology was investigated and sorted out. Firstly， smart contract vulnerability types were elaborated and analyzed systematically from two aspects： logic and interaction. Then， characteristics and limitations of the existing smart contract vulnerability detection methods， including static analysis， symbolic execution， fuzzy detection， and deep learning， were summarized， and 30 vulnerability detection tools were summed up and compared. Finally， the opportunities and challenges faced in the current development of smart contract vulnerability detection technology were discussed， and the future research directions in this field were prospected with the combination of deep learning technology.