The concept of Quality of Aquaculture Services (QoAS) is inspired by the Quality of Service (QoS) principle, originally developed in the field of networks and telecommunications, where it refers to the ability to guarantee the quality, availability, and priority of service in a communications system. Adapted to the aquaculture context, QoAS is fundamental to maximising the benefits of Integrated Multi-Trophic Aquaculture (IMTA). IMTA has emerged as a sustainable approach to meet the growing global demand for aquatic food products by combining species from different trophic levels in a single system, optimising resource use, improving environmental performance, and diversifying production. However, ensuring QoAS in these complex systems requires the implementation of advanced technologies to monitor, manage, and optimise every aspect of the aquaculture process. This article presents a comprehensive review of technologies applied at IMTA, focusing on IoT-based monitoring systems, resource management algorithms, water recirculation technologies, intelligent automation, biosecurity, and data management platforms. Our review finds that IoT and automation-based solutions significantly enhance real-time monitoring, increasing operational efficiency and environmental sustainability. Key challenges identified include integration complexity, high costs, and technical expertise requirements, but the ongoing development of modular, user-friendly solutions indicates a promising trajectory. This review highlights the transformative role of technological innovation in IMTA, providing a foundation for future research and advancements in QoAS management in aquaculture.

Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality worldwide among both men and women. Early and accurate detection is essential to improve patient outcomes. This study explores the use of artificial intelligence (AI)-based software for the diagnosis of lung cancer through the analysis of medical images in DICOM format, aiming to enhance image visualization, preprocessing, and diagnostic precision in chest computed tomography (CT) scans. Methods: The proposed system processes DICOM medical images converted to standard formats (JPG or PNG) for preprocessing and analysis. An ensemble of classical machine learning algorithms—including Random Forest, Gradient Boosting, Support Vector Machine, and K-Nearest Neighbors—was implemented to classify pulmonary images and predict the likelihood of malignancy. Image normalization, denoising, segmentation, and feature extraction were performed to improve model reliability and reproducibility. Results: The AI-enhanced system demonstrated substantial improvements in diagnostic accuracy and robustness compared with individual classifiers. The ensemble model achieved a classification accuracy exceeding 90%, highlighting its effectiveness in identifying malignant and non-malignant lung nodules. Conclusions: The findings indicate that AI-assisted CT image processing can significantly contribute to the early detection of lung cancer. The proposed methodology enhances diagnostic confidence, supports clinical decision-making, and represents a viable step toward integrating AI into radiological workflows for early cancer screening.

The advancement of technology has made digital education a vital tool for global access to quality education, making it necessary to have comprehensive regulations and public policies to ensure ethical use. These protect the rights of students and educators, data confidentiality and institutional integrity, while promoting equity and inclusion. Global efforts are focused on developing regulatory frameworks and integrating policies to improve the quality of education across borders. New education policies, such as the UN Sustainable Development Goals, highlight the potential of technology to improve education. This study examines the challenges and opportunities of integrating global education policy with technology to improve educational outcomes around the world, with the participation of policymakers, educators, and technology specialists. Reviewing the European Union, Latin America, and nations with the highest educational standards such as Finland, South Korea, and Singapore.

A smart city is a city that uses the optimum management of water, city technology, energy, mobility, public transportation, distribution of transport routes, road network safety management, the construction of homes / offices / transportation structure, industry / jobs, education, healthcare, public safety, government, civil protection, food, environmental pollution, and solid, hazardous and special waste This chapter will analyse how smart cities can become catalysts for urban sustainability through the implementation of advanced technologies and the integration of innovative strategies. With a focus on carbon footprint reduction, energy efficiency and climate change resilience, it will explore various initiatives and policies that are transforming cities towards a greener and more equitable future.

Smart cities aim to integrate technological, infrastructural, and socio-environmental systems in order to improve urban sustainability and quality of life. To qualify as both smart and sustainable, a city is generally expected to pursue self-sufficiency through the adoption of sustainable practices in energy production, water supply, and food systems. Such cities also seek to reduce operational costs for both private operators and municipalities, while aiming to enhance the quality of life of their residents. Within this context, the relevance of a web-based application becomes particularly apparent. An application equipped with predefined indicators can provide a structured and measurable framework for assessing the current status of a city or town in relation to smart and sustainable development. This framework allows for the evaluation of the extent to which a city aligns with established criteria associated with smart and sustainable urban models. This paper introduces a Python-based web application, developed using Python version 3.10, designed to assess or support the self-assessment of a city’s alignment with identified smart and sustainable development indicators. This study does not claim empirical validation or benchmarking performance; the proposed system is presented as a proof-of-concept framework. The work does not propose new smart city indicators. Rather, it presents an integrative system that seeks to operationalise existing smart and sustainable city indicators within a unified and modular web-based assessment framework, designed to support cross-domain evaluation and citizen-accessible self-assessment.

A model-based topological digital twin is developed for warehouse processes by joining MBSE requirements, synchronized event streams, persistent homology, Mapper graphs, and operational indicators. Synthetic data enable regeneration, controlled parameters, and internal validation; they do not support causal or industrial-performance claims. The illustrative experiment is extended across 30 seeds, point-cloud and Mapper sensitivity analyses, and two conventional comparators. Reconfiguration reduces dwell time and queue length while increasing throughput. Its topological effects are less uniform: several H_{2} measures and alert counts improve, but H_{1} indicators remain sensitive to sampling and do not consistently return to baseline. KPI rules identify disruption, whereas PCA also marks reconfigured states. Topology therefore supplies complementary rather than universally superior evidence. The pipeline produces auditable outputs suitable for future VR/AR integration, although neither an immersive interface nor a user study is implemented.