Predictive models for delay in medical decision-making among older patients with acute ischemic stroke: a comparative study using logistic regression analysis and lightGBM algorithm

The components of vocational education talents and the relationship between the development of handicraft and vocational education are examined in this study. Next, using the independent samples t-test, it examines the general state of vocational school students’ recognition of quality as well as the variations in craftsmanship levels between students with varying majors, genders, grades, and internship experiences. Then, using Pearson’s correlation coefficient method, the relationship between students’ craftsmanship and the theoretical curriculum teachers, practical activities, and other factors at the school was investigated. The multivariate linear regression model was used to analyze the factors that influence the cultivation effect of craftsmanship. The road map for developing craftsmanship in vocational education is finally developed. According to the study, there is a significant positive correlation between all the factors and students’ artisan spirit. The correlation coefficients between the campus environment, theoretical courses taught at the school, teachers, practical activities, personal career planning, and personal artisan literacy are, respectively, 0.55, 0.56, 0.57, 0.99, and 0.53, all of which are greater than 0. This study offers concrete and workable methods for fostering craftsmanship in today’s students, which is crucial for enhancing talent development and advancing the growth of the manufacturing sector.

Cultivation and Implementation of Craftsmanship in Vocational Education in the Context of the New Era

2024

Research on the Integration and Development of Cultural Creative Industry and Rural Tourism Industry in the Information Age

Ma,

Wang

2024

This paper constructs the innovative development model of cultural and tourism integration based on the digital development process of cultural and creative industries and the rural tourism industry under the background of informationization. Then, it analyzes the comprehensive development level, coupled and coordinated development characteristics, and regional differences of cultural and creative industries and rural tourism industries in the whole country and 7 regions by using the coupled and coordinated degree model. Finally, the effectiveness of the digital development model of cultural and tourism integration is analyzed through the multiple linear regression model, and the moderating effect of the digital development model of cultural and tourism integration on the regional service capacity, infrastructure, and economic development is explored in depth. The results show that for every 1 percentage point increase in the level of digitalized development of cultural and tourism integration, the employment in the regional tertiary industry increases by 1.5%, the level of infrastructure development increases by 1.6%, and the level of local economic development grows by 1.2%. This study enriches and improves the content framework of the research on the high-quality development of rural tourism and provides new analytical ideas for the research on the high-quality development of rural tourism and cultural and creative industries.

Improvement of Inventory Management and Demand Forecasting by Big Data Analytics in Supply Chain

Tang

2024

Inventory management plays a very important role in the process of business operation, providing favorable backing for the smooth operation of production and sales. In this paper, LightGBM and PSO-LSTM models in big data technology are combined to improve inventory management and demand forecasting in supply chains. Then, the relationship between inventory, order, and forecast is elaborated, the two-level inventory cost components and the relationship between them are analyzed, the model constraints are formulated, and a mathematical model for two-level multi-cycle inventory control is constructed. Finally, the single demand forecasting model is compared with the improved model to explore the optimization effect of inventory management after the application of the LightGBM-PSO-LSTM model. The LightGBMPSO-LSTM model is the best fit and can be used for actual demand forecasting. After the optimization of inventory management, the inventory turnover ratio of Company H increased from 8.2 in 2022 to the maximum value of 9.2 in 2023, and the OTIF achievement rate of sales orders increased from 97.9% in 2022 to 99.3% in 2023. This paper provides a successful example of optimizing supply chain inventory management using big data analytics.