Financial forecasting has been greatly improved in recent years, but at long horizons, forecast accuracy may be low. Foreign trade plays an important role in introducing advanced technology and equipment, expanding employment opportunities, increasing government revenue and promoting economic growth. The main purpose of this paper is to predict the export volume of foreign trade through a back-propagation neural network (BPNN). To shed light on the characteristics of foreign trade and the export volume calculation method, this paper uses BPNN for forecasting. This method has a unique and advanced advantage in solving nonlinear problems and is very suitable for solving forecasting and decision-making problems related to nonlinear financial systems. By establishing multifactor and single-factor export forecasting models, the export volume of a single Chinese city in recent years is forecasted and compared with the actual export volume. The forecasting accuracy of our model is more than 30% higher than that of the traditional forecasting method, and the application is also approximately 15% more accurate than the traditional method, indicating that the method used in this paper is more in line with the growth trend of the actual export data. As a key part of the economic system, foreign trade is an important force driving economic growth. Therefore, developing foreign trade is a suitable path to pursue growth.

In this paper, a novel analytical platform for the visual, sensitive and reliable analysis of mercury ions (Hg2+) is fabricated based on functionalized doped quantum dots. We synthesized new specific...

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Countryside planning has become popular due to the improvement in the economic level of China. A rural construction planning permission system is an important means to guide and standardize village construction. Therefore, this study investigates the current condition of rural planning in Guangdong and the general condition of Guangdong Province. Village planning problems, such as the village theory, lack of characteristics, and lack of coordination, are also presented. The bottleneck of the construction village planning permission system is presented. A “three-step” strategy and mode transformation (i.e., legal, personalized, and independent steps) is established based on the analysis of the Guangdong rural planning problems. Finally, the general requirements for village construction under the permission system are proposed along with the study of the village planning in Guangdong, which is the representative case. Therefore, this study provides a reference for the effective linkage between village planning and the rural construction planning permission system.

Binocular stereo matching, a computer vision task typically using cost volume constructed from the left and right feature maps to estimate disparity and depth, is widely applied in 3D reconstruction, autonomous driving and robotics navigation. Though recent study brings an awareness of the convolution neural networks and the attention algorithms used in this field can make great progress, it is still difficult to satisfy the demand of high-precision applications due to many reasons. Study finds that the exist methods usually incline to ignore the intermediate feature map of other scales, pay less attention to the relationship between left and right feature maps and even just tend to use one type of cost volume to train the model. In this article, we mainly focus on solving the three problems mentioned above. Firstly, we present the Multi-scale Feature Extraction and Fusion Module(MFEFM) to get the informational feature maps via fusing all scale feature maps. And then we design the Effective Channel Attention Module(ECAM) applied to better capture and utilize the channel-wise independencies. Finally, we adopt the Hybrid Cost Volume Computation Module(HCVCM) to construct and aggregate cost volume. With these solutions, we build an end-to-end stereo matching network named HCVNet. Comparison with other state-of-the-art models, it can achieve 0.714px EPE on SceneFlow dataset, descending PSMNet(1.09px EPE) by 37.6%.INDEX TERMS binocular stereo matching, feature map, channel-wise independencies, channel attention, cost volume