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.

This paper mainly discusses the application and impact of AI tools in vocational college students' career planning and employment preparation in Chinese Mainland. Through a review and analysis of relevant literature, this article found that artificial intelligence tools can provide students with more information and assistance, thereby improving their career cognition and employment competitiveness. However, if artificial intelligence tools are not open to Chinese users or students overly rely on these tools, it may also bring some negative effects, such as job anxiety and decreased self-awareness. Therefore, the government and teaching departments should strengthen the education of career planning and employment preparation, improve the artificial intelligence system, establish personalized service mode and other measures to provide more comprehensive and personalized career recommendation and employment services for higher vocational students in Chinese Mainland.

This study explores the determinants of political participation among Thai youth, focusing on the roles of political interest, knowledge, and efficacy. Employing stratified random sampling, data were collected from 191 university students in Bangkok. Structural Equation Modeling (SEM) via Smart PLS was utilized to test hypotheses regarding the direct and mediating effects of political interest and knowledge on participation, highlighting the mediating role of political efficacy. The findings indicate that political efficacy significantly enhances participation, while political interest boosts knowledge significantly but does not directly influence efficacy. Furthermore, political knowledge positively affects efficacy but not participation directly. Notably, the indirect effects of political interest on participation through efficacy alone are insignificant, but the pathways from interest to participation through both knowledge and efficacy, and from knowledge to participation through efficacy, are significant. These results elucidate the complex interactions between political interest, knowledge, and efficacy in shaping the political engagement of Thai youth.

Global economic competition is leading companies to improve their competitiveness by increasing production and eliminating the main obstacles to the process of making products available. This approach concerns both SMEs and SMIs as well as multinationals. Thus, the Compagnie Minière de l’Ogooué (COMILOG), a subsidiary of the French group ERAMET, which until recently had a monopoly on manganese mining in Gabon, must now face competition from Asian operators. To export its ore, COMILOG must first transport it by rail for nearly 650 km, from the Moanda site (south-east of the country) to the port of Owendo. However, port operations, which until then took place exclusively during the day, limited the company’s export capacities and the profits made, while increasing the stopover time of ships and their operating costs. To remedy this, the French company introduced nighttime docking and departures. This work addresses the challenges of the performance of port operations at the Owendo ore terminal and the security and natural risks of night manoeuvres. The general objective of the study is to assess the impact of these night services on ship traffic, on the one hand, and to identify the related socio-economic and security issues, on the other hand. Data collection was carried out using documentary research in libraries and research centres, consultation of websites, semi-directed interviews, questionnaire surveys and participatory observation. The sample of 50 people surveyed took into account management staff, supervisors and line managers, integrating the diversity of actors involved in the processing of ships calling at the port of Owendo. Finally, the surveys attest to a clear reduction in the time spent by ships at the Owendo Ore Port and an increase in their number calling. They also confirm the improvement in tonnages embarked and the improvement in turnover achieved by COMILOG. This study led to the conclusion that the introduction of night manoeuvres at the port of Owendo allowed COMILOG to increase its exports and the number of ore carriers received in stopover and then improve its turnover.

Coordination and integration among farms within agri-food chains are crucial to tackle the issue of fragmentation within the primary sector, both at the European and national level. The Italian agri-food system still complains about the need to aggregate supply to support market dynamics, especially for niche and quality products that characterize the Made in Italy. It is well known that the Italian agri-food sector is closely linked to the relationship between agriculture on one hand and culture/tradition on the other, which is reflected in the high number of quality products that have obtained EU PDO (Protected Designation of Origin) and PGI (Protected Geographical Indication) recognition. The development of vertical forms of coordination has found significant support in recent years from the integrated supply chain design approach, which is increasingly becoming an essential tool for implementing rural development policies. In this context, the study provides a comparison between companies that have joined the Integrated Supply Chain Projects of the Rural Development Program and those that have not applied. The aim is to highlight any differences in order to understand policy impact. The analysis is based on the Emilia-Romagna region Farm Accountancy Data Network (FADN) data, and the sample consists of more than 2 thousand farms. The statistical analysis conducted compares treated and non-treated using the Welch-t-test for independent unmatched samples. The main results show higher values for treated farms when structural variables are analyzed, like the utilized agricultural area or the agricultural work unit. In general, higher balance sheet performances emerged for treated farms. In conclusion, this study shows that the Integrated Supply Chain Projects represent a worthwhile tool both to increase cooperation, food quality, and to enhance a competitive agricultural sector.