Abstract
This study examines the potential applications of large language models (LLMs) and intelligent agents in educational environments, with a particular focus on their role in enhancing the quality of teaching and learning processes. It provides a comprehensive overview of LLMs, emphasizing their capabilities in natural language analysis and generation. Furthermore, the study examines the potential for collaboration between LLMs and intelligent agents. While LLMs offer a foundation for AI capabilities, intelligent agents utilize these technologies to perform autonomous and context-aware actions within educational systems. A comparative analysis of various intelligent agent platforms, including Autogen, Langra, Crew AI, LM Studio, and Olama, constitutes a central component of this research. This study addresses the criteria that informed the selection of Crew AI for a case study, with a particular focus on its adaptability, ease of integration, and task execution capabilities in comparison to the other platforms. The research includes an analysis of the platform's performance in a controlled educational environment, highlighting the advantages of Crew AI in system functionality. These results demonstrate the necessity for a strategic and well-structured approach to integrating LLMs and intelligent agents, as their successful implementation can foster new competencies, enhance stakeholder engagement, and offer innovative teaching and learning experiences.

Abstract
Distributed Hash Tables (DHTs) remain to this day a central component of modern peer-to-peer (P2P) systems, which rely on complex DHT protocols to scale to millions of nodes. The correct operation of DHTs is therefore essential for the proper functioning of these systems. For this reason, formal methods have been employed to model and verify a range of correctness properties of various DHT protocols. However, these verification efforts have focused on protocol-specific properties, such as topological invariants, instead of functional properties. This focus limits our understanding of the precise guarantees offered by each protocol. We propose a protocol-independent axiomatization of DHT properties using Allen Temporal Logic (ATL). To validate our axiomatization, we have implemented it in the Alloy analyser and used our implementation both to establish a number of DHT-derived properties and to generate a set of DHT execution traces that cover an exhaustive list of DHT corner case behaviours.

Abstract
We present PolyNarrative, a new multilingual dataset of news articles, annotated for narratives. Narratives are overt or implicit claims, recurring across articles and languages, promoting a specific interpretation or viewpoint on an ongoing topic, often propagating mis/disinformation. We developed two-level taxonomies with coarse- and fine-grained narrative labels for two domains: (i) climate change and (ii) the military conflict between Ukraine and Russia. We collected news articles in four languages (Bulgarian, English, Portuguese, and Russian) related to the two domains and manually annotated them at the paragraph level. We make the dataset publicly available, along with experimental results of several strong baselines that assign narrative labels to news articles at the paragraph or the document level. We believe that this dataset will foster research in narrative detection and enable new research directions towards more multi-domain and highly granular narrative related tasks.

Abstract
Recommendation systems can play an important role in today's digital content platforms by supporting the suggestion of relevant content in a personalised manner for each customer. Such content customisation has not been consistent across most media domains, and particularly on radio streaming and gaming aggregators, which are the two real-world application domains focused in this work. The challenges faced in these application areas are the dynamic nature of user preferences and the difficulty of generating recommendations for less popular content, due to the overwhelming choice and polarisation of available top content. We present the design and implementation of a Reinforcement Learning-based Recommendation System (RLRS) for web applications, using a Deep Deterministic Policy Gradient (DDPG) agent and, as a reward function, a weighted sum of the user Click Distribution (CD) across the recommended items and the Dwell Time (DT), a measure of the time users spend interacting with those items. Our system has been deployed in real production scenarios with preliminary but promising results. Several metrics are used to track the effectiveness of our approach, such as content coverage, category diversity, and intra-list similarity. In both scenarios tested, the system shows consistent improvement and adaptability over time, reinforcing its applicability.

Abstract
Adopting innovative technologies such as blockchain and additive manufacturing can help organisations promote the development of additive symbiotic networks, thus pursuing higher sustainable goals and implementing circular economy strategies. These symbiotic networks correspond to industrial symbiosis networks in which wastes and by-products from other industries are incorporated into additive manufacturing processes. The adoption of blockchain technology in such a context is still in a nascent stage. Using the case study method, this research demonstrates the adoption of blockchain technology in an additive symbiotic network of a real-life context. The requirements to use a blockchain network are identified, and an architecture based on smart contracts is proposed as an enabler of the additive symbiotic network under study. The proposed solution uses the Hyperledger Fabric Attribute-Based Access Control as the distributed ledger technology. Even though this solution is still in the proof-of-concept stage, the results show that adopting it would allow the elimination of intermediary entities, keep available tracking records of the resources exchanged, and improve trust among the symbiotic stakeholders (that do not have any trust or cooperation mechanisms established before the symbiotic relationship). This study highlights that the complexity associated with introducing a novel technology and the technology's immaturity compared to other data storage technologies are some of the main challenges related to using blockchain technology in additive symbiotic networks.

Abstract
Higher Education Institutions (HEIs) face increasing pressure to transform fragmented information environments into cohesive, data-driven ecosystems that support strategic and operational decision-making. This study proposes a comprehensive framework for implementing Business Intelligence (BI) in HEIs, evolving from a validated roadmap to an integrated ecosystem perspective. Grounded in the Design Science Research methodology, the work combines a systematic literature review, the design of a flexible BI architecture, and an in-depth case study at the University of Tr & aacute;s-os-Montes and Alto Douro (UTAD). The framework addresses critical factors such as strategic alignment, data governance, and system interoperability, and demonstrates how dashboards and analytics can enhance institutional intelligence and evidence-based management. Results from the UTAD case confirm the framework's capacity to overcome technical and organisational barriers, enabling the transition from isolated systems to intelligent, interconnected data infrastructures. This research contributes to the literature by bridging theoretical guidelines and practical implementation, providing a scalable reference model to guide BI-driven digital transformation in higher education. It also demonstrates the tangible institutional value of integrated BI ecosystems in supporting more informed, timely, and efficient decision-making.