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.

The study’s objectives are to investigate the relationships between earnings management, government ownership, and corporate performance in the Gulf Cooperation Council (GCC) region during the period 2017–2021, utilizing a dataset comprising 188 companies. It further explores the moderating role of government ownership in the association between earnings management and company performance. The study used the panel regression data analysis to investigate the relationship between the variables under the study. Employing linear regression and moderated linear regression, the research discerns notable patterns. The result shows a positive effect emerges between government ownership and corporate performance. Conversely, the result shows a negative association is observed between earnings management and corporate performance. Finally, the moderating role of government ownership in GCC countries is a good governance mechanism to mitigate the agency problem.

The contraction of manufacturing economic activity in Latin American countries has been affected by the health crisis in the last few years. This phenomenon has negatively impacted the Latin American countries’ economies. In order to evaluate the impact of the manufacturing economy, this research integrates the impact of Foreign Direct Investment (FDI) on the growth of the Ecuadorian manufacturing sector, from 1981 to 2019, considering the role of the state through public spending using cointegration. The results are not consistent considering the empirical framework used; thus, FDI has a negative and significant influence on the manufacturing sector. Also, the manufacturing sector has a strong relationship with FDI in the short run and a less significant one in the long run. The results presented in this research suggest promoting domestic and FDI in the manufacturing sector, not only towards overexploited and monopolized sectors such as mining and telecommunications.

This study explores the factors affecting dentists’ willingness to use social media in their practices, examining how consumer behavior influences their adoption decisions. Despite the growing use of social media across industries, its adoption in dentistry remains relatively underexplored. As investments in digital technologies increase, understanding dentists’ intentions to integrate social media becomes crucial, especially considering the evolving consumer behavior patterns in healthcare. Using the Technology Acceptance Model (TAM) and factoring in patient pressures, this study analyzes data from 209 respondents through SPSS and Smart PLS 4.0. The results offer valuable insights for dentists, highlighting the benefits of social media integration, and justifying investments in these platforms to align with changing consumer expectations. The study also discusses its limitations and suggests future research directions to further explore social media adoption in dentistry and its potential to drive economic growth within the sector.