Competency-based education is one of the many important educational objectives in the cultivation of senior vocational talents. In the past education model, the importance of achievement is greater than ability. Teachers rely on the scores of test papers to classify students' grades. Competency-based education has changed this situation very well, paying special attention to students' ability training. This paper mainly studies how to better promote the reform and innovation of English teaching in higher vocational colleges and strengthen students' learning ability and vocational skills while ensuring students' ability development.

In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.

Since the proposal of the low-carbon economy plan, all countries have deeply realized that the economic model of high energy and high emission poses a threat to human life. Therefore, in order to enable the economy to have a longer-term development and comply with international low-carbon policies, enterprises need to speed up the transformation from a high-carbon to a low-carbon economy. Unfortunately, due to the massive volume of data, developing a low-carbon economic enterprise management model might be challenging, and there is no way to get more precise forecast data. This study tackles the challenge of developing a low-carbon enterprise management mode based on the grey digital paradigm, with the aim of finding solutions to these issues. This paper adopts the method of grey digital model, analyzes the strategy of the enterprise to build the model, and makes a comparative experiment on the accuracy and performance of the model in this paper. The results show that the values of MAPE, MSE and MAE of the model in this paper are the lowest. And the r^2 of the model in this paper is also the highest. The MAPE value of the model in this paper is 0.275, the MSE is 0.001, and the MAE is 0.003. These three indicators are much lower than other models, indicating that the model has high prediction accuracy. r² is 0.9997, which is much higher than other models, indicating that the performance of this model is superior. With the support of this model, the efficiency of building an enterprise model has been effectively improved. As a result, developing an enterprise management model for the low-carbon economy based on the gray numerical model can offer businesses new perspectives into how to quicken the shift to the low-carbon economy.

Lithospermum extract from Lithospermum is a kind of naphthoquinone, which has good anti-ultraviolet and anti-bacterial function. In this paper, the effects of different treatment temperature, time and ratio of liquid to liquid on the UV resistance of Lithospermum erythrorhizon extract were studied. The optimum extraction conditions were as follows: extraction temperature 60 ℃, extraction time 2 h, ratio of liquid to liquid of Lithospermum and ethanol 1:11. In this paper, the anti-UV finishing of cotton fabric was carried out, and the anti-ultraviolet and whiteness of the fabric were taken as the main indexes. The optimum process of the anti-UV finishing was as follows: the impregnation temperature was 70 ℃, the immersion time was 2h, 1:40. Compared with the uncoated cotton fabric, the fabric UPF value of the fabric was improved from 12.31 to 83.25, and the anti-ultraviolet performance was excellent, and it had certain bacteriostatic effect on Bacillus subtilis and Escherichia coli.

This study critically examines the multifaceted dynamics of foreign employee integration within the Czech Republic, with a specific focus on the Mladá Boleslav region. Conducted prior to the Ukrainian crisis, this research serves as a crucial baseline for understanding integration in a pre-crisis context and provides comparative insights into the evolving challenges and opportunities amid the subsequent migration movements. The study explores various aspects of integration and inclusion, drawing upon migration theories, economic factors, and sociological perspectives to understand the motivators and challenges faced by foreigners, particularly in light of the majority society’s perception, which often leans towards skepticism and negativity. The research methodology builds on grounded theory and integrates both quantitative and qualitative approaches, utilizing surveys and semi-structured interviews to explore the experiences of foreign nationals, with an emphasis on immigrant women. A key finding of the study is the significant role of employers in facilitating integration. The paper discusses how businesses, through inclusive policies and practices, can profoundly influence the integration experience. Cooperation between employers, local integration centers, and other relevant organizations emerges as vital, providing additional resources and support systems to enhance the integration process. The study concludes by emphasizing the critical role of various stakeholders, particularly employers, in shaping sustainable human resources practices that foster a more inclusive and harmonious society.