Abstract
Recently, there has been growing attention on the decarbonisation of maritime transport, particularly regarding the landside operations at ports. This has spurred the development and implementation of strategies and policies aimed at enhancing the environmental performance of port activities. Among these strategies, the electrification of port infrastructure is emerging as a potential industry standard for the future. However, there remains a significant gap in understanding the patterns of electricity consumption in ports and how to forecast them accurately. To address this gap, this paper provides a review of the current literature on electricity demand in ports, examining practical applications, methodologies employed, and their key limitations. The findings indicate that, despite its importance in supporting the electrification process, electricity demand forecasting in ports has not received substantial attention in either industry or academic research, and there are no clearly established policies to support port authorities in obtaining the necessary data. Finally, the paper outlines potential directions for future research and how port authorities or local government agencies can contribute to these efforts.

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Abstract
Data-based approach for diagnosis of thyroid disorders is still at its early stage. Most of the research outcomes deal with binary classification of the disorders, i.e. presence or not of some pathology (cancer, hyperthyroidism, hypothyroidism, etc.). In this paper we explore deep learning (DL) models to improve the multi-class diagnosis of thyroid disorders, namely hypothyroid, hyperthyroid and no pathology thyroid. The proposed DL models, including DNN, CNN, LSTM, and a hybrid CNN-LSTM architecture, are inspired by state-of-the-art work and demonstrate superior performance, largely due to careful feature selection and the application of SMOTE for class balancing prior to model training. Our experiments show that the CNN-LSTM model achieved the highest overall accuracy of 99%, with precision, recall, and F1-scores all exceeding 92% across the three classes. The use of SMOTE for class balancing improved most of the model’s performance. These results indicate that the proposed DL models not only effectively distinguish between different thyroid conditions but also hold promise for practical implementation in clinical settings, potentially supporting healthcare professionals in more accurate and efficient diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Students spend 30 % of their lives indoors; therefore, a healthy indoor air quality (IAQ) is crucial for their well-being and academic performance in Higher Education Institutions. This review highlights the interventions for improving Indoor Enviclassrooms considering climate change by discussing ventilation techniques, phytoremediation, and building features designed to improve noise levels, thermal comfort, lighting and to reduce odor. Awareness and literacy are enhanced through the student's engagement by offering real-time monitoring knowledge of Indoor Environmental Quality using inexpensive smart sensors combined with IoT technology. Eco-friendly strategies are also highlighted to promote sustainability.

Abstract
Artificial Intelligence has recently expanded across various applications. Machine Learning, a subset of Artificial Intelligence, is a powerful technique for identifying patterns in data to support decision making and managing the increasing volume of information. Simultaneously, Digital Twins have been applied in several fields. In this context, combining Digital Twins, Machine Learning, and Smart Buildings offers significant potential to improve energy efficiency and operational effectiveness in building management. This review aims to identify and analyze studies that explore the application of Machine Learning and Digital Twins for operation and energy management in Smart Buildings, providing an updated perspective on these rapidly evolving topics. The methodology follows the PRISMA guidelines for systematic reviews, using Scopus and Web of Science databases. This review identifies the main concepts, objectives, and trends emerging from the literature. Furthermore, the findings confirm the recent growth in research combining Machine Learning and Digital Twins for building management, revealing diverse approaches, tools, methods, and challenges. Finally, this paper highlights existing research gaps and outlines opportunities for future investigation.

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