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  • Item type: Publicação , Access status: Acesso aberto ,
    Experiencia
    (2026) Seg, Teres; GS, C
  • Item type: Item , Access status: Acesso aberto ,
    Blockchain-powered traceability in the wine industry: Enhancing transparency and consumer trust
    (2026) Cordeiro, M.; Ferreira, J. C.; Elvas, L.; Fernandes, V.
    This study presents a blockchain-based traceability system designed to enhance transparency, combat counterfeiting, and strengthen consumer trust within the wine industry. The proposed solution employs a private MultiChain blockchain using a Proof of Authority (PoA) consensus mechanism to ensure data integrity, immutability, and accountability across the supply chain. Smart contracts are integrated to automate critical processes such as product registration, quality verification, and ownership transfers, while QR codes allow consumers to access the complete lifecycle history of each wine bottle. The system was developed and evaluated following the Design Science Research Methodology (DSRM) and tested in a real-world wine supply chain in the Douro Valley, Portugal. Results demonstrate substantial improvements in traceability, regulatory compliance, and operational efficiency, including a 75 % reduction in regulatory audit time and significantly higher consumer trust scores in a controlled field survey (n = 372). This research contributes a replicable and scalable framework for decentralized product identity management in agrifood supply chains, offering actionable insights for industry stakeholders and enriching the theoretical discourse on blockchain-enabled transparency in agriculture.
  • Item type: Item , Access status: Acesso aberto ,
    Governance mechanisms for mitigating AI challenges to human intelligence: A conceptual framework analysis
    (2025) Moreira, O.; Rodrigues, M. C.; Sousa, M.; Filho, A.
    This study proposes a dynamic governance model to guide the interaction between artificial intelligence (AI) and human intelligence. It focuses on mechanisms that mitigate AI's impact on human cognition while promoting innovation, ethical responsibility, and alignment with the Sustainable Development Goals (SDGs) of the 2030 Agenda. Using a mixed-methods approach, the study combines a narrative literature review (2019–2024) from Web of Science, ScienceDirect, and Research Rabbit with a structured development process. The model comprises three interconnected blocks: Governance, Human Intelligence, and Social Intelligence, each linked to specific SDGs. The Governance block emphasizes the responsible use of AI through regulatory policies that promote algorithmic fairness and data governance, aligning with SDGs 9 and 16. It also advocates for AI literacy and ethics education to foster inclusion and equality (SDGs 4 and 10) and implements safeguards such as transparency and accountability to ensure the ethical use of AI (SDGs 9 and 16). The Human Intelligence block examines AI's cognitive impacts, balancing cognitive augmentation in problem-solving (SDGs 4 and 9) with caution against the risks of over-reliance, such as cognitive erosion (SDG 4). The Social block highlights AI system assessments to enhance adaptation, inclusion, and equitable access (SDGs 10 and 16) while encouraging policy adjustments for sustainable innovation (SDGs 9 and 17). The study concludes that integrating these blocks is essential for achieving fair, inclusive, and sustainable technological development. Structured governance, incorporating ethical principles, social reform, and continuous monitoring, ensures the responsible adoption of AI, supporting the harmonious interaction between AI and human intelligence in achieving the Sustainable Development Goals (SDGs).