The Tan‐Lu Fault Zone is a major fault system in eastern China, and its timing of activity has been the focus of much research. We studied a ductile segment of the Tan‐Lu Fault Zone, the Malongshan Shear Zone, which is located in the Feidong Complex (the southern part of the Zhangbaling Uplift). A dark biotite‐adamellite mylonite has been recognized in the Malongshan Shear Zone, and the mylonitic foliation strikes NNE, consistent with the regional orientation of the Tan‐Lu Fault Zone. The occurrence of abundant syn‐tectonic leucocratic veins within the biotite‐adamellite mylonite indicates that ductile shearing was accompanied by hydrothermal activity. To constrain the timing of deformation along the Malongshan Shear Zone, we dated the biotite‐adamellite mylonite and the leucocratic veins using zircon U–Pb geochronology. The weighted mean ages of zircon grains from the biotite‐adamellite mylonite (159.0 ± 5.5 Ma) and the captured Group 2 (156.3 ± 3.7 Ma and 173.2 ± 4.2 Ma) of leucocratic veins are interpreted as the ages of protoliths of the biotite‐adamellite mylonite during the Jurassic. Additional weighted mean age of the hydrothermal zircon grains (130.9 ± 3.6 Ma) represents the timing of crystallization of the veins. Therefore, the results indicate that the Malongshan Shear Zone (and hence the southern segment of the Tan‐Lu Fault Zone) was active during the Early Cretaceous (~131 Ma). Based on the present results and existing data, we consider that sinistral shearing of the Tan‐Lu Fault Zone during the Early Cretaceous was controlled by oblique subduction of the Pacific Plate at a high angle.
Through empirical research, it is found that the traditional autoregressive integrated moving average (ARIMA) model has a large deviation for the forecasting of high-frequency financial time series. With the improvement in storage capacity and computing power of high-frequency financial time series, this paper combines the traditional ARIMA model with the deep learning model to forecast high-frequency financial time series. It not only preserves the theoretical basis of the traditional model and characterizes the linear relationship, but also can characterize the nonlinear relationship of the error term according to the deep learning model. The empirical study of Monte Carlo numerical simulation and CSI 300 index in China show that, compared with ARIMA, support vector machine (SVM), long short-term memory (LSTM) and ARIMA-SVM models, the improved ARIMA model based on LSTM not only improves the forecasting accuracy of the single ARIMA model in both fitting and forecasting, but also reduces the computational complexity of only a single deep learning model. The improved ARIMA model based on deep learning not only enriches the models for the forecasting of time series, but also provides effective tools for high-frequency strategy design to reduce the investment risks of stock index.
The accurate segmentation of retinal blood vessels in fundus is of great practical significance to help doctors diagnose fundus diseases. Aiming to solve the problems of serious segmentation errors and low accuracy in traditional retinal segmentation, a scheme based on the combination of U-Net and Dense-Net was proposed. Firstly, the vascular feature information was enhanced by fusion limited contrast histogram equalization, median filtering, data normalization and multi-scale morphological transformation, and the artifact was corrected by adaptive gamma correction. Secondly, the randomly extracted image blocks are used as training data to increase the data and improve the generalization ability. Thirdly, stochastic gradient descent was used to optimize the Dice loss function to improve the segmentation accuracy. Finally, the Dense-U-net model was used for segmentation. The specificity, accuracy, sensitivity and AUC of this algorithm are 0.9896, 0.9698, 0.7931, 0.8946 and 0.9738, respectively. The proposed method improves the segmentation accuracy of vessels and the segmentation of small vessels.
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