Abstract
This paper examines the role of digital twins (DTs) in promoting sustainability within seaport operations and logistics. DTs have emerged as promising tools for enhancing seaport performance. Despite the recognized potential of DTs in seaports, there is a paucity of research on their practical implementation and impact on seaport sustainability. Through a systematic literature review, this study seeks to elucidate how DTs contribute to the sustainability of seaports and to identify future research and practical applications. We reviewed and categorized 68 conceptual and practical digital applications into ten core areas that effectively support economic, social, and environmental objectives in seaports. Furthermore, this paper proposes five preliminary potential applications for DTs where practical implementations are currently lacking. The primary findings indicate that DTs can enhance seaport sustainability by facilitating real-time monitoring and decision-making, improving safety and security, optimizing resource utilization, enhancing collaboration and communication, and supporting the development of the seaport ecosystem. Additionally, this study addresses the challenges associated with DT implementation, including high costs, conflicting stakeholder priorities, data quality and availability, and model validation. The paper concludes with a discussion of the implications for seaport managers and policymakers.

Abstract
Marine aquaculture, particularly in the Mediterranean region, faces the challenge of minimizing growth dispersion, which has a direct impact on the production cycle, market value and sustainability of the sector. Conventional grading methods are resource intensive and potentially detrimental to fish health. The current study presented an innovative approach in predicting fish weight dispersion in European seabass (Dicentrarchus labrax) aquaculture. Seabass is one of the two major fish species cultivated on the Mediterranean coast, with a fattening cycle of 18-24 months. During this period, several grading operations are carried out to minimize growth dispersion. The intricate feed-fish-water system, characterized by complex interactions among feeding regimes, fish behavior, individual metabolism and environmental factors, is the focus of the study. The comprehensive, five-step methodology addresses this complexity. The process begins with a Discrete Event System (DES) model that simulates the feed-fish-water dynamics, taking into account individual fish metabolism. This is followed by the development of a surrogate machine learning (ML) regressor model, which is trained on DES simulation data to efficiently predict growth distribution. The model is then calibrated and customized for specific fish stocks and production tanks. The preliminary results from 21 tanks in two trials with European seabass (D. labrax) showed the effectiveness of the method. The results from the simulation models achieved a R2 of 99.9 % and a Mean Absolute Percentage Error (MAPE) of 1.1 % for the prediction of mean final weight and a R2 of 90.3 % with a MAPE of 8.1 % for the standard deviation of final weight. In summary, this study represents a significant advance in the planning and management of seabass aquaculture. Given the lack of effective prediction tools in the aquaculture industry, the proposed methodology has the potential to reduce risks and inefficiencies, thus possibly optimizing aquaculture practices by increasing sustainability and profitability.

Abstract
Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87% spam F-measure.

Abstract
The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity. Antenna defects, ranging from manufacturing imperfections to environmental wear, pose significant challenges to the reliability and performance of communication systems. This review paper navigates the landscape of antenna defect detection, emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection. This review paper serves as a valuable resource for researchers, engineers, and practitioners engaged in the design and maintenance of communication systems. The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures. In this study, a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented. The PRISMA principles will be followed throughout the review, and its goals are to provide a summary of recent research, identify relevant computer vision techniques, and evaluate how effective these techniques are in discovering defects during inspections. It contains articles from scholarly journals as well as papers presented at conferences up until June 2023. This research utilized search phrases that were relevant, and papers were chosen based on whether or not they met certain inclusion and exclusion criteria. In this study, several different computer vision approaches, such as feature extraction and defect classification, are broken down and analyzed. Additionally, their applicability and performance are discussed. The review highlights the significance of utilizing a wide variety of datasets and measurement criteria. The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation, such as real-time inspection systems and multispectral imaging. This review, on its whole, offers a complete study of computer vision approaches for quality control in antenna parts. It does so by providing helpful insights and drawing attention to areas that require additional exploration.

Abstract
Human-Robot Collaboration (HRC) in advanced industrial scenarios has emerged as a transformative force. Modern robots, infused with artificial intelligence (AI), can enhance human capabilities, offering a wide spectrum of opportunities in agriculture, forestry, construction and many other domains. However, the complex nature of HRC demands realistic simulators to bridge the gap between theory and practice. This paper introduces the FEROX Simulator, purpose-built for robot-assisted wild berry collection. We briefly delve into the simulator's capabilities to showcase its potential as a platform to develop HRC systems. Our research underscores the need for simulators designed for challenging HRC contexts and aims to inspire further advancements in this domain.

Abstract
Predictors of macroeconomic indicators rely primarily on traditional data sourced from National Statistical Offices. However, new data sources made available from recent technological advancements, namely data from online activities, have the potential to bring about fresh perspectives on monitoring economic activities and enhance the accuracy of forecasting. This paper reviews the literature on predicting macroeconomic indicators, such as the gross domestic product, unemployment rate, consumer price index or private consumption, based on online activity data sourced from Google Trends, Twitter (rebranded to X) and mobile devices. Based on a systematic search of publications indexed on the Web of Science and Scopus databases, the analysis of a final set of 56 publications covers the publication history of the data sources, the methods used to model the data and the predictive accuracy of information from such data sources. The paper also discusses the limitations and challenges of using online activity data for macroeconomic predictions. The review concludes that online activity data can be a valuable source of information for predicting macroeconomic indicators. However, one must consider certain limitations and challenges to improve the models' accuracy and reliability. © 2024 - IOS Press. All rights reserved.