The Predicament and Countermeasures of China’s 1+X Certificates system: Based on the Perspective of Stakeholders

China’s skilled talent evaluation certificates mainly include vocational qualification certificates, vocational skill level certificates and special vocational ability certificates, but they are not aimed at college students. In 2019, China launched the pilot of the 1+X certificate systems, proposing to develop vocational skill level certificates for college students, so as to improve the quality of vocational education in China. From the perspective of stakeholder theory, X certificates are of great significance to students, teachers, colleges, and employers. However, at present, X certificates still have some problems, such as insufficient authority for evaluation organizations, weak construction of teacher team, insufficient teaching supply and insufficient coverage of certificates. In order to improve the effectiveness of 1+X certificates, it is necessary to improve the authority of vocational skill level certificates, restructure the teaching staff, increase the types of X certificates, and restructure the curriculum system.

Study on vibration characteristics of rolling mill based on vibration absorber

The vertical vibration often occurs during the rolling production, which has an influence on the accuracy of rolling mill. In order to effectively suppress the vertical vibration of the rolling equipment, the rolling mill model with vibration absorber device was established. Based on the main resonance singularity of the rolling mill system, the best combination of opening parameters was obtained. The best combination of opening parameters helps the rolling mill system work in a stable area. Finally, the effects of different vibration absorber parameters on the vibration characteristics of the rolling mill system were analyzed. Results show that the vibration absorber device can effectively improve the stability of the rolling mill system

DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification

Tomato is an important and fragile crop. During the course of its development, it is frequently contaminated with bacteria or viruses. Tomato leaf diseases may be detected quickly and accurately, resulting in increased productivity and quality. Because of the intricate development environment of tomatoes and their inconspicuous disease spot features and small spot area, present machine vision approaches fail to reliably recognize tomato leaves. As a result, this research proposes a novel paradigm for detecting tomato leaf disease. The INLM (integration nonlocal means) filtering algorithm, for example, decreases the interference of surrounding noise on the features. Then, utilizing ResNeXt50 as the backbone, we create DCCAM-MRNet, a novel tomato image recognition network. Dilated Convolution (DC) was employed in STAGE 1 of the DCCAM-MRNet to extend the network\u27s perceptual area and locate the scattered disease spots on tomato leaves. The coordinate attention (CA) mechanism is then introduced to record cross-channel information and direction- and position-sensitive data, allowing the network to more accurately detect localized tomato disease spots. Finally, we offer a mixed residual connection (MRC) technique that combines residual block (RS-Block) and transformed residual block (TR-Block) (TRS-Block). This strategy can increase the network\u27s accuracy while also reducing its size. The DCCAM-classification MRNet\u27s accuracy is 94.3 percent, which is higher than the existing network, and the number of parameters is 0.11 M lesser than the backbone network ResNeXt50, according to the experimental results. As a result, combining INLM and DCCAM-MRNet to identify tomato diseases is a successful strategy

DS-MENet for the Classification of Citrus Disease

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life