Parthenope University of Naples
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Cultural Heritage in the Mediterranean Regions: A Literature Review in the Sustainable and Smart Perspective
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Innovation at the Edge of Chaos: Frictions in the Education Ecosystem in the Age of GenAI
No full textThis paper investigates how universities are approaching the integration of Generative AI (GenAI) within teaching practices, focusing on the ongoing transformation of the broader educational service ecosystem. Drawing from a qualitative, exploratory study grounded in the Gioia methodology, we analyze institutional policies, experiential practices, and stakeholder perceptions—along with reflexive insights from an AI agent (ChatGPT)—to explore how GenAI is framed, encouraged, or restricted. While universities engage in multiple missions—teaching, research, and third mission—this study deliberately focuses on teaching, a domain where contradictions are most visible, and where the tensions triggered by GenAI unfold with greater immediacy, according to both faculty and students that have been interviewed. The paper argues that the higher education service ecosystem is approaching a state of “edge of chaos,” a condition of free complexity in which roles, norms, and value creation practices are being redefined or the ecosystem will collapse. The contribution offers theoretical and managerial insights on how universities might navigate this liminal state to remain relevant and generative actors in the future education ecosystem
Enhanced Cybersecurity Monitoring in Multi-Plant Flexible Manufacturing Environments
No full textThis paper proposes a cybersecurity monitoring framework tailored to multi-plant Flexible Manufacturing Systems. The framework combines Digital Twin technology, hierarchical SIEM and SOAR systems, and AI-based incident response. Addressing the limitations of traditional cybersecurity methods in distributed manufacturing scenarios, the proposed solution enables real-time threat detection, cross-facility correlation of events, automated incident responses, and an integrated threat-sharing platform. Initial evaluations indicate that the approach improves early anomaly detection and reduces false positives in threat detection. Ongoing and future research steps include incorporating advanced AI agents for automated mitigation, expanding simulations to more sophisticated attack vectors, optimizing system performance, minimizing false positives, and conducting comprehensive validation using various industrial protocols to ensure compliance with cybersecurity standards
1. Maltrattamento e abuso del minore nello sport: contesti socio-educativi ed evidenze empiriche, in BioLaw Journal, Rivista di BioDiritto, Università di Trento, 4/25, DOI 10.15168/2284-4503-20254, ISSN 2284-4503
No full textL’esperienza sportiva, nell’arco della vita e soprattutto durante l’adolescenza, può favorire la crescita personale se sostenuta da adulti consapevoli e ambienti educativi sicuri. Tuttavia, dinamiche culturali e organizzative possono esporre i minori a pressioni, fino a generare forme di maltrattamento o abuso nei contesti sportivi sia amatoriali sia agonistici. Alcune forme, come trascuratezza e abuso psicologico, sono particolarmente difficili da rilevare, poiché spesso sono ne-gate o minimizzate dagli stessi atleti. È necessario garantire ambienti orientati al benessere e alla tutela dei diritti dei minori, aumentando la consapevolezza sui rischi. La presenza adulta, intesa come guida non direttiva, è cruciale per promuovere esperienze sportive inclusive e in grado di sostenere percorsi generativi di crescite sane
Magnetoencephalographic source localization and reconstruction via deep learning
No full textWithin this manuscript a deep learning algorithm designed to achieve both spatial and temporal source reconstruction based on signals captured by MEG devices is introduced. Brain signal estimation at source level is a significant challenge in magnetoencephalographic (MEG) data processing. Traditional algorithms offer excellent temporal resolution but are limited in spatial resolution due to the inherent ill-posed nature of the problem. Nevertheless, many applications require precise localization of pathological tissues to provide reliable information for clinicians. In this context, deep learning solutions emerge as promising candidates for high resolution signals estimations. The proposed approach, termed “Deep-MEG,” employs a hybrid neural network architecture capable of extracting both temporal and spatial information from signals captured by MEG sensors. The algorithm is capable to handling the entire brain and, therefore, is not limited to cortical sources imaging. To validate its efficacy, the Authors conducted simulations involving multiple active sources using a realistic forward model, and subsequently compared the results with those obtained using various state-of-the-art reconstruction algorithms. Finally Deep-MEG has been tested also with real MEG data
