Abstract

Abstract
Carbon neutralization is a European concern, which is why the maritime sector should implement strategies to reduce greenhouse gas (GHG) emissions, particularly in port areas. The Port of Sines, a very important maritime hub in Portugal, is in a stage of significant expansion including new terminal constructions and renewable energy projects, which amplify its energy demands. This paper presents a new approached of a load model, consumption Integration of renewable energy sources and energy storage systems for the Port of Sines, analysing a global hourly energy consumption in two months of 2023. Using a Software Package MATLAB, the details of the consumption profiles of all ships and terminals in order to identify periods of peak demand, the information on the integration of renewable energy sources and energy storage systems, will be studied and analysed. Due to the increase in maritime traffic and the use of potential Onshore Power Supply Systems (OPS) to reduce emissions, in this study a new energy requirements will be analysed. This new model will be as a step for optimizing the port's electrical infrastructure, enhancing energy efficiency, and supporting sustainable growth. Finally, some conclusions that provide a valuable contribution to the understanding of the Portuguese Ports, aims to provide a critical study of the load model to be taken, into account when managing port energy demand and advancing environmental goals are pointed out.

Abstract
We propose a unified architecture for visual scene understanding, aimed at overcoming the limitations of traditional, fragmented approaches in computer vision. Our work focuses on creating a system that accurately and coherently interprets visual scenes, with the ultimate goal to provide a 3D virtual representation, which is particularly useful for applications in virtual and augmented reality. By integrating various visual and semantic processing tasks into a single, adaptable framework, our architecture simplifies the design process, ensuring a seamless and consistent scene interpretation. This is particularly important in complex systems that rely on 3D synthesis, as the need for precise and semantically coherent scene descriptions keeps on growing. Our unified approach addresses these challenges, offering a flexible and efficient solution. We demonstrate the practical effectiveness of our architecture through a proof-of-concept system and explore its potential in various application domains, proving its value in advancing the field of computer vision.

Abstract
Over the years, various immersive virtual training environments (iVTEs) have been developed, allowing companies to start transitioning to Virtual Reality (VR) technologies to train their personnel. This transition forces companies to start using game engines as a foundation to develop such iVTEs, which also requires a multidisciplinary team. When developing such training environments, challenges on how to present tasks to users arise. The way these tasks are presented can dictate the efficacy of the VR training application. This paper presents three different task presentation methodologies (avatar animation, videos, and instruction manual) and assesses them using 36 participants, divided into those three groups. Usability, sense of presence, satisfaction, cybersickness, and technology acceptance variables were studied and results indicated that only the total number of actions performed had differences between groups where the instruction manual reported the higher number of actions (usability) when compared to the other conditions. Therefore it was concluded that the instruction manual proved to be where users kept losing focus and making more actions. It was also concluded that all conditions had a similar sense of presence, satisfaction, cybersickness, and acceptance scores.

Abstract

Abstract
This paper presents a comprehensive study on the application of machine learning techniques in the prediction of respiratory rate via time-series-based statistical and machine learning methods using several physiological signals. Two different models, ARIMA and LSTM, were developed. The LSTM model showed a stronger capacity for learning and capturing complicated patterns in the data compared to the ARIMA model. The findings imply that LSTM models, by incorporating many variables, have the ability to provide predictions that are more accurate, particularly in situations where respiratory rate values vary significantly. © 2024 The Authors. Published by ELSEVIER B.V.