Design and research of computer network micro-course management system based on JSP technology

The Application of Mental Health Teaching Method and Special Teaching Method in College Chinese Teaching under the Network Environment

In order to improve the quality of College Chinese teaching, innovate the teaching mode, enrich the teaching content, and fundamentally improve the application and practice ability of college students so as to promote the reform of College Chinese teaching, this article briefly describes the research background of this subject, introduces the special teaching mode under the network environment, and briefly describes the basic content and practice of the special teaching method. This article mainly explores the construction of a network environment based on blockchain technology, including the construction of a blockchain framework and the application of the blockchain system. This article briefly analyzes the functional requirements of the network environment, explores the application practice of the mental health teaching method and the special subject teaching method based on the network environment, and finally studies the creation and implementation of the network environment conditions of the special subject teaching method. By creating a high-quality network environment, it provides a technical guarantee for the application and realization of special teaching method, effectively meets the needs of College Chinese teaching, and cultivates students' interest. Under the joint action of the above algorithms, the BTP congestion control system is verified by the laboratory simulation environment: it has more than 40% random packet loss resistance, the bandwidth is still available as low as 300 kbps, and the network jitter is 1200 ms. It has been proved that the mental health teaching method, the special subject teaching method, and the network environment have remarkable application effects in College Chinese teaching and are worthy of wide application and promotion.

CNN-Based Cross-Modal Residual Network for Image Synthesis

This study attempts to address the issue that present cross-modal image synthesis algorithms do not capture the spatial and structural information of human tissues effectively. As a consequence, the resulting photos include flaws including fuzzy edges and a poor signal-to-noise ratio. The authors offer a cross-sectional technique that combines residual modules with generative adversarial networks. The approach incorporates an enhanced residual initial module and attention mechanism into the generator network, reducing the number of parameters and improving the generator's feature learning capabilities. To boost discriminant performance, the discriminator employs a multiscale discriminator. A multilevel structural similarity loss is included in the loss function to improve picture contrast preservation. On the ADNI data set, the algorithm is compared to the mainstream algorithms. The experimental findings reveal that the synthetic PET image's MAE index has dropped while the SSIM and PSNR indexes have improved. The experimental findings suggest that the proposed model may maintain picture structural information while improving image quality in both visual and objective measures. The residue initial module and attention mechanism are employed to increase the generator's capacity for learning, while the multiscale discriminator is utilized to improve the model's discriminative performance. The enhanced method in this study can maintain the structure and contrast information of the picture, according to comparative experimental findings using the ADNI dataset. The produced picture is hence more aesthetically similar to the genuine print.