Abstract

Abstract
In a world that is in constant changing and where carbon neutrality becomes a common objective, it is necessary to implement European policies and targets to reduce greenhouse gas emissions. The maritime sector is one of the most polluting in the world, becoming mandatory to implement technologies in port area to reduce their footprint. Most of the good's transportation are made by sea, the maritime industry is growing, and the biggest chair of greenhouse gas emission comes from shipping. The seaport has the role to implement solutions to reduce the emissions in port area, allowing the ships to shutdown their engines while they are moored in port. Renewable energy production alongside with shore power supply systems are becoming a common solution in ports as some of the technologies that allows to reduce ships emissions in port area. This paper presents the state of the art of onshore power supply in ports and standards related to shore power supply and data requirements for load model built and emissions calculations.

Abstract

Abstract
Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, advances in FMs can find uses in electric power grids, challenged by the energy transition and climate change. This paper calls for the development of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. It is argued that FMs learning from diverse grid data and topologies, which we call grid foundation models (GridFMs), could unlock transformative capabilities, pioneering a new approach to leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a practical implementation pathway and road map of a GridFM-v0, a first GridFM for power flow applications based on graph neural networks, and explore how various downstream use cases will benefit from this model and future GridFMs.

Abstract
This paper advocates for the integration of system identification in graduate-level control system courses using accessible theoretical tools. Emphasising real-world applications, particularly in Remotely Operated Vehicle (ROV), the study proposes ROV as educational platforms for teaching control principles. As a concrete example, the paper presents a graduation course project focusing on designing a depth control system for an ROV, where students derive the model from experimental data. This practical application not only enhances the students skills in system identification but also prepares them for challenges in controlling complex systems in both academic and industrial settings.

Abstract
Immersive experiences offered by virtual reality (VR) have the power to impact tourists' decision-making and on-site experiences. However, prior research has focused on explaining VR's acceptance by tourists as a function of technological capacity, rather than user characteristics, such as innovativeness. This research intends to fill the existing knowledge gap regarding the role of technological innovativeness on VR experiences. To do so, this study examines whether individuals' technological innovativeness influences their perceptions of a VR tourism experience and, in turn, their intention to recommend the experience. The study provides a VR experience to 405 tourists at a winery. The results reveal that the tourists' technological innovativeness influences their perception of enjoyment, experience quality, and satisfaction. These findings suggest that, when developing or distributing VR content, the technological innovativeness of the audience is important to consider. The findings have theoretical and practical value, with direct implications for tourism professionals and policymakers.