Conference Agenda

The optimization of maintenance in transportation fleets, combined with sustainable logistics approaches and compliance with standards such as ISO 14001, has shown significant progress in both practice and theory. This review aims to integrate operational efficiency with environmental sustainability, establishing Lean methodologies and sustainable logistics for proper management, and analyzing the benefits of ISO 14001 that arise from developing established methodologies. 1,735 articles in Scopus, EBSCO, and Web of Science include research from 2020 to the current year, 2025. In this sense, logistics organizations can significantly reduce their emissions through internal strategies, such as switching fuels in their fleets and implementing renewable electricity management in their facilities. The conclusion is that by integrating green logistics, Lean methodologies, and ISO 14001, we achieve environmental change in global organizations, as well as satisfactory economic results.

Mining is an environment that demands increased operational efficiency, sustainability, and cost reduction, where digitalization is emerging as a crucial and strategic solution. The purpose of this systematic review is to evaluate the use of machine learning and digital twins in relation to the operational efficiency of mining plant equipment. Methodologies such as PRISMA ensure the effectiveness of the selection process for the articles considered, allowing the analysis to be structured using the PICO strategy, considering equipment characteristics, ML-DT integration mechanisms, and the results obtained after its implementation. The search was conducted using search engines such as Scopus and SciencieDirect, filtering publications that were not published between the years 2019 and 2025, obtaining a total of 74 articles that met the inclusion criteria. The findings revealed a growing trend in the use of these technologies to optimize processes such as flotation, conveying, predictive monitoring, and mill energy control. Overall, significant improvements were identified in reducing energy consumption, lowering maintenance costs, and increasing equipment availability and reliability. Furthermore, ML-based predictive models demonstrated high accuracy in early fault detection and real-time operational decision-making. In short, the integration of Machine Learning and Digital Twin in mining plants represents a key advance toward more efficient, safe, and sustainable operations. This technological synergy not only optimizes equipment performance but also paves the way for a more competitive and resilient mining industry in the face of future challenges

This study aimed to: identify the attribute factors related to the image of a marine protected area and determine which of these factors explain tourist satisfaction and behavioral loyalty. The study was conducted in the Galápagos Islands, a marine protected area in Ecuador, South America. A total of 407 tourist questionnaires were collected on-site, including both domestic and international visitors, and factor analysis techniques along with multiple linear regressions were applied. The findings reveal four factors in the image attributes of marine protected areas: Staff Attention, Tourist Facilities, Nature, and Cultural Activities. Among the factors that explain satisfaction with the tourist destination, as well as the intention to recommend it and speak positively about it, Nature and Staff Attention at the destination were identified. Meanwhile, the Cultural Activities factor best explains the intentions to revisit the destination. These results can be useful for marine protected area managers in developing sustainable management plans

The food industry faces growing challenges in efficiency, sustainability, and adaptability to variable demands. Artificial intelligence (AI) is emerging as a transformative solution for the supply chain, optimizing demand prediction, quality control, and waste reduction. This Systematic Literature Review analyzes studies published between 2020 and 2025 on AI applications—such as machine learning, deep learning, and explainable models—in the
food industry.
Using the PICOC approach and the Scopus database, 49 relevant studies were selected. The results show that AI improves key stages: shelf-life prediction, automation with computer vision, and monitoring through digital twins, increasing efficiency and sustainability. However, limitations persist, such as accessibility for small businesses and the need for more transparent models.
This research provides a structured overview of the use of AI, identifies predominant technologies, and suggests future lines of work focused on scalability and explainable AI, adding value to agrifood management.

Tourist activities on the coast are varied and include aspects related to culture, the beach, the sun, nature, and social life. This study aimed to achieve the following objectives: (i) identify the motivations of tourists and visitors; (ii) determine the demand segments; and (iii) identify the relationship between demand segmentation, satisfaction, and loyalty in coastal tourism. The study was conducted in situ in the city of Montañita, Ecuador, a renowned destination for surfing and water sports, visited by both domestic and international tourists. The sample consisted of 380 valid questionnaires. The techniques employed included factor analysis, the K-means clustering method, and Pearson's chi-square test. Five motivational dimensions were identified: culture and nature, novelty and social interaction, sun and beach, and sports and entertainment. Two clearly differentiated segments were identified: the multi-motive tourist and the sun and beach tourist. The multi-motive group showed a higher level of expectation fulfillment, satisfaction, and loyalty in coastal tourism. The results will be useful for destination managers and tourism service providers, as well as contributing to existing academic knowledge on coastal tourism