Gustavo Isaza

Efficient maritime route planning is pivotal for optimising global logistics operations in an increasingly competitive environment. This study aims to determine the optimal number of clusters among 90 georeferenced ports across six continents, minimizing distances and enhancing operational efficiency through advanced clustering techniques. Methods such as geographic k-means, Gaussian Mixture Models (GMM), and hierarchical clustering were implemented and evaluated using quality metrics like the Calinski-Harabasz, Silhouette, and Davies-Bouldin indices. The results identified 13 optimal clusters that reflect logical and geographically coherent segmentation, highlighting geographic k-means for its consistency and replicability. Additionally, integrating an intelligent routing model enabled the design of optimised routes within each cluster, reducing distances and maximizing logistical efficiency. The proposed methodology, developed in Python, demonstrates not only its applicability to maritime operations but also its potential for extrapolation to other contexts, showcasing robustness and relevance for global strategic planning. This approach represents a significant advancement in integrating clustering and optimisation techniques to enhance maritime supply chain management.

The challenges of optimizing logistics operations in all links of the supply chain have led to the development of new dynamics around the revolution 4.0 and the response of operational efficiency linked to environmental sustainability. Smart ports are born as a strategy to meet customer needs from a technological evolution that generates quality logistical and operational responses. The objective of this review is to identify and to analyze the research perspectives related to smart ports. The implemented methodology considered a scientific mapping to determine the most relevant publications in terms of authors, journals, and countries with the greatest scientific participation in the subject and a network analysis based on the implementation of the tree metaphor of the graph theory. The R-studio software and the Bibliometrix plugin were used to process the information. The review considered 204 documents from the Scopus and Web of Science databases, identifying a growing trend in the number of enhanced publications as of 2019, with China being the country with the largest number of papers. In relation to research trends, the adaptation of ports to industry 4.0, maritime ports and technological security, and green and smart ports are the perspectives on the subject of study. Finally, an agenda for future research is presented.

The operations of international trade drive global economies, with maritime transport serving as a key pillar in market connectivity and acceleration. The opportunities presented by Industry 4.0 to connect supply chain actors through integrated logistics pose a challenge that fuels the number of studies in this area, with a particular interest in the transportation stage due to its responsibilities for efficiency. This study conducts a systematic literature review by analyzing scientometric aspects and identifying research trends related to maritime transportation logistics. The PRISMA methodology, graph theory, and bibliometric indicators such as indegree, betweenness, and outdegree were employed to process the information related to maritime transportation logistics from the Scopus and Web of Science databases spanning from 2000 to 2022. The gathered information and results were processed and analyzed using R-studio and Bibliometrix tools. The documents were classified as original, structural, and research trends, with the latter focused on topics related to the environment, port competitiveness, logistics optimization, and economics. Finally, an agenda for future research in the field is presented.

Conventional methodologies employed in detecting distributed denial‐of‐service attacks have frequently struggled to adapt to the dynamic and multi‐faceted evolution of such threats. Furthermore, many of the contemporary detection and prevention solutions, while innovative, remain anchored to dedicated workstations, lacking the flexibility and scalability required in today's digital landscape. To bridge this technological chasm, this research introduces a state‐of‐the‐art intrusion detection system firmly rooted in advanced Deep Learning techniques. By leveraging the expansive and adaptable nature of cloud‐centric, service‐oriented architectures, we not only bolster detection precision but also offer a solution designed for modern infrastructures. This system provides enterprises with a robust, easily deployable tool that is both versatile in its application and proactive in its defence approach, ensuring that networks remain resilient against the continuously evolving spectrum of cyber threats.