Issue 6 (November)

Dvořák's Cello Concerto is one of the most famous cello concertos, known for its beautiful and majestic melodies. Performing this concerto requires a high level of technical proficiency from the player. Personally, I am very fond of this piece, and in this article, I aim to analyze several critical and challenging techniques in the concerto. My goal is to help others better understand the logic of performance and improve their technical skills. Great music performance is built on a foundation of solid technique. Only by thoroughly studying and mastering technical issues can we better develop our personal style and interpret the emotions the composer intended to convey. I hope this article will be helpful to those practicing Dvořák's Cello Concerto.

With the rapid development of science and technology, the intelligent network automobile industry is an important direction in the future transportation field.It has attracted wide attention.As an important city in western China, Chongqing also has broad prospects for development in the field of intelligent network automobile.With the promotion of the construction of Chongqing's "33618" modern manufacturing cluster system, Chongqing's automobile industry has developed vigorously.However, the development of Chongqing Intelligent Network United Automobile is also facing some difficulties, including insufficient technological innovation and insufficient market demand.Lack of talent and so on. The purpose of this paper is to analyze the current development dilemma of Chongqing intelligent network automobile industry cluster, and put forward the corresponding countermeasures.In order to promote the healthy development of this field.

Tongue recognition plays a pivotal role in traditional Chinese medicine (TCM), and there have been many deep learning studies applied to tongue recognition, but there are fewer studies on lightweight tongue recognition, and the use of a lightweight network to apply tongue recognition to mobile has a pivotal role in the digitalisation and standardisation development of TCM, as well as monitoring and warning of people's health conditions, and a deep learning-based lightweight recognition method is the key to achieve mobile deployment. In this research paper, an efficient hybrid tongue image recognition network (SESAViT), a novel neural network integrating convolutional neural network and VIT, is proposed. We found that in the image recognition task, although VIT shows advantages in extracting global information, it is computationally intensive, so we simplify the computational VIT approach by using the sandwich layout, segmented attention module, making it possible to get rid of the limitation of VIT's multi-head self-attention mechanism, and we call the model SAViT.For the convolutional neural network part, we use the token mixer and channel mixer separation to achieve lightweight improvement. Our experiments on a homemade tongue dataset demonstrate the superiority of our method, with tongue colour, tongue moss features, tongue body features, tongue shape, dentate tongue and cleft tongue classification accuracies of 86.284%, 82.467%, 83.417%, 86.259%, 84.759% and 85.759%, respectively, which is much better than that of lightweight networks based on VIT and MobileNet v3 by about 3%.