Hospital performance possesses strategic significance in achieving an essential completive advantage for the public hospitals. This study aimed to examine the relationship between patient safety culture (PSC) and the performance of traditional Chinese medicine (TCM) public hospitals in Sichuan, China. To address the research purpose, this study analyses the hospital performance and Patient safety culture in traditional Chinese medicine public hospital in China. We examine the propose model by analyzing cross-sectional survey data from 194 clinical directors at 194 public traditional Chinese medicine hospitals using the Partial least squares structural equation model in Smart PLS 4.0. This study provides predictive evidence that PSC in unit management and management support can lead to better patient safety outcomes. The results revealed patient safety outcomes significantly and positively effects of patient safety related to unit management and management support on overall hospital performance (p-value: 0.000–0.003).

SMEs are characterized by a number of flaws that threaten their survival and counteract them from reaching high levels of growth and development. Access to finance is the primary problem facing these companies in the Moroccan context. Aware of the effective and potential impacts of SMEs on the country as a whole, the Moroccan Government through a variety of actors has mobilized its efforts in a number of ways to support this population of companies. This study assesses the extent to which actors within the Moroccan SMEs’ financing ecosystem align to support these companies and develop their ability to access external financing. Using the MACTOR model, based on an in-depth contextual analysis and expert interviews, our findings suggest that Morocco’s SMEs’ financing ecosystem is skewed, with high levels of convergence between its components.

In contemporary English teaching in primary and secondary schools, good use of modern educational technology can greatly improve the efficiency of teachers' teaching and students' learning, especially during the epidemic period, the application of educational technology in teaching has become an indispensable topic. As the guider of students, teachers should have more mature modern education concepts, master various advanced teaching technologies, prevent the use of "formalism" in educational technology, and ensure that network resources can have a positive impact on students' learning efficiency and effect. This paper adopts the methods of field investigation, interview and literature analysis to investigate and study the current situation of the application of modern educational technology in English teaching in Jinhe Middle School in Genhe City, analyze the existing problems, and propose targeted solutions, in order to effectively apply modern educational technology in Jinhe Middle School and improve its English teaching efficiency and effect.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.