The maritime industry has a significant influence on the global economy, underscoring the need for operational availability and safety through effective maintenance practices. Predictive maintenance emerges as a promising solution compared to conventional maintenance schemes currently employed by the industry, offering proactive failure predictions, reduced downtime events, and extended machinery lifespan. This paper addresses a critical gap in the existing literature by providing a comprehensive overview of the main data-driven PdM systems. Specifically, the review explores common issues found in vessel components (i.e., propulsion, auxiliary, electric, hull), examining how different state-of-the-art PdM architectures, ranging from basic machine learning models to advanced deep learning techniques aim to address them. Additionally, the concepts of centralized machine learning, federated, and transfer learning are also discussed, demonstrating their potential to enhance PdM systems as well as their limitations. Finally, the current challenges hindering adoption are discussed, together with the future directions to advance implementation in the field.

The present contribution aims to give an insight into the main terrestrial and marine processes leading to delta flooding in the case of the transboundary Evros delta, located at the microtidal NE Aegean Sea, on the basis of recorded flood events in the Evros deltaic plain. The prevailing weather conditions at the onset of the event, along with sea-level rise above the mean state, portray the mechanism for the development of compound flood events and subsequent riparian flooding. This system blocks the riverine water’s seaward exit, resulting in the flooding of the lower deltaic plain. The river discharge is recognized as a secondary factor acting mainly toward the persistence of the events. Several limitations restrict the quantification potential of the relative contribution of the key factors to the development, onset, and duration of a flood. Mitigation of the impacts of such flood events requires intercountry cooperation and a management plan based on a network of environmental monitoring.

Research infrastructures have been established throughout Europe in order to create robust organizations that will facilitate and enhance research and innovation processes and will advance society with innovative products and services. The Hellenic Integrated Marine Observing, Forecasting and Technology System (component of HIMIOFoTS RI) has been implemented in the framework of the National Roadmap for Research Infrastructures to form a large-scale infrastructure for the marine environment in Greece. It links together ocean observing and forecasting systems, coastal zone monitoring and management practices, as well as ocean engineering testing facilities. The overarching framework of the system supports the coordination of five organizations with expertise in the field of marine science and technology, the central management of research activities, and the common development of services and products. It comprises facilities and resources while it provides open access to research communities (academia, industry) to support the scientific advancements and innovation in their fields. The Hellenic Marine Observing, Forecasting and Technology System was further enhanced during its implementation through significant upgrades and developments in order to extend its observing capacity and the forecasting and technological abilities, while advancing the provided services and products.