This study aims to structure guidelines for an intervention model from the perspective of Integral Project Management to improve the competitiveness level of cacao associations in south region of Colombia. The research followed a mixed-method approach with a non-experimental cross-sectional design and a descriptive scope. The study employed a stage-based analytical framework which included: identifying the factors influencing the competitiveness of the cacao sector; grouping these factors under the six primary determinants of competitiveness with reference to Porter’s Diamond Model; and proposing guidelines for an intervention model to enhance the competitiveness of the studied associations through project management. The first stage was conducted via literature review. The second stage involved primary data collected through surveys and interviews with the associations, members, and cacao sector experts in Huila. The third stage entailed grouping the factors within the main determinants that promote and limit the competitiveness of the cacao sector in the context of Porter’s Diamond Model. Based on the analysis of the corresponding restrictive and promoting factors, strategic recommendations were formulated for the various sector stakeholders on the measures that can be adopted to address restrictive factors and maintain promoting factors to enhance and sustain the sector's competitiveness.

Over the last few decades, we have experienced a remarkable evolution of technologies, with a consequent impact on the modes of transport used. These developments have made all modes of transport more accessible. This study examines the evolution of transport in the European Union. To this end, we analysed the international framework, followed by the general legal framework and the type of transport sector at the European level. Furthermore, we examined areas where improvements could be made, facilitating a subsequent review of other key aspects of transport. This enabled us to identify a series of future actions to improve accessible transport in Europe.

Social media has become one of the primary sources of communication, information, entertainment, and learning for users. Children gain several benefits as social media helps them acquire formal and informal learning opportunities. This research also examined the effect of social media on formal and informal learning among school-level children in Ajman, United Arab Emirates (UAE), moderated by social integrative and personal integrative needs. Data was gathered by using structured questionnaires, which were distributed among a sample of 364 children. Results revealed that social media significantly affects Informal and formal learning among children, indicating its usefulness in child education and development. The results also indicated a significant moderation of social integrative needs on social media’s direct effect on informal learning, indicating the relevant needs as an important motivating factor. However, the moderation of personal integrative needs on social media’s direct effect on formal learning remained insignificant. Overall, this research highlighted the role of social media in providing learning opportunities for children in the UAE. It is concluded that children actively seek gratifications from social media, shaping their learning within structured educational contexts in their daily lives. Through the lens of UGT, certain needs play a critical role in strengthening the gratification process, affecting how children derive learning advantages from their interactions on social media platforms. Finally, implications and limitations are discussed accordingly.

Raising public awareness of maritime risk and disseminating information about disaster prevention and reduction are the most frequent ways that the government incorporates citizens in marine disaster risk management (DRM). However, these measures are deemed to be insufficient to drive the participation rate. This study aims to understand the participation trend of citizens in marine DRM. On the basis of the theory of citizen participation’s ladder, public participation within marine DRM is categorized into non-participation, tokenistic participation, and substantive participation. Using organization theory, the government’s strategies for encouraging participation are classified into common approach (raising awareness), structural approach (innovating instruments), and cultural approach (developing citizenship). Considering the vignette experiment of 403 citizens in a coastal city of China that has historically been subject to marine disasters, it was found that effectiveness of the strategies, from highest to lowest, are citizenship development, risk education, and instruments innovation. At the individual level, psychological characteristics such as trust in the government, past disaster experience, and knowledge of marine DRM did not significantly influence citizens’ participation preferences. At the government level, even when citizens are informed about new participatory mechanisms and tools, they still tend to be unwilling to share responsibilities. However, self-efficacy and understanding the beneficial outcomes of their participation in marine (DRM) can positively impact the willingness to participate. The results show that to encourage public participation substantively in the marine DRM, it is important to cultivate a sense of civic duty and enhance citizens’ sense of ownership, fostering a closer and more equitable partnership between the state and society.

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.