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- The social representation of the governance system through key descriptors: mute zone?Publication . Marchisotti, Gustavo; Filho, Jose; Franca, Sergio; Domingos, Maria; Junior, Vicente; Toledo, Roberto; Toledo, Roberto; Alves, Cátia; Castro, Helio; Putnik, GoranThis article seeks to describe the social representations of Brazilians about the term Governance System (GS). The data were collected through online questionnaires answered by 665 social actors from Brazil. The data analysis used the Social Representation Theory (SRT), operationalized by the techniques of free evocation of words and the Four Houses Framework of Verges, followed by lexical and content analysis. It was identified that in the center of the table there are words highly shared by the social actors about the Governance System: Accountability, Administration, Compliance, Control, Management, Organization, Planning, Processes, Transparency, and Ethics. It is concluded that Accountability is conceived as a structuring element for the effectiveness of the GS. The data suggest the existence of a mute zone in the social representation, since there was a scarcity of words that brought negative expressions about the GS and that deserve future investigations.
- Hybrid governance system value perception modelPublication . Marchisotti, Gustavo; Filho, Jose Rodrigues; Franca, Sergio; Toledo, Roberto; Castro, Helio; Alves, Cátia; Putnik, GoranThis paper aims to analyze the negative perception about the ability to generate value from a Governance System (GS). A model that would explain the reason for the less positive perception regarding the ability to generate value from the GS of the organizations, from the analysis of the relationship between the constructs Hybridism, GS, Accountability and Perception of Value based on IR, was proposed and validated with structural equation modeling (SEM), based on 658 responses from professionals of Brazilian organizations from the public, private and non-profit sectors. It is suggested that conflicts related to organizational hybridism negatively influence the results orientation of the GS, which in turn influences the imbalance of its Accountability. As a result of the GS's loss of results orientation, and considering the IR capitals in the disclosure of results, there is a negative perception of the GS's ability to add value to the results.
- A review of applied artificial intelligence in manufacturing: Emergent AI models in cyber–physical systems for manufacturingPublication . Rocha Varela, Maria Leonilde; Manupati, Vijaya Kumar; Pinheiro, Pedro; Putnik, Goran; Ferreira, Luís; Alves, Cátia; Ávila, Paulo; Castro, HelioThe integration of artificial intelligence (AI) is a cornerstone of Industry 4.0, driving significant gains in automation, efficiency, and adaptability. In parallel, manufacturing environments are evolving into cyber–physical systems (CPS), where physical processes are deeply integrated with computational intelligence. While machine learning and deep learning techniques have become standard practice in manufacturing CPS, the emergence of advanced and foundation AI models—such as reinforcement learning, agent-based AI systems, large language models, and neuro-symbolic approaches—brings fresh opportunities and challenges that are not fully understandable. This paper offers a comprehensive systematic literature review (SLR) on AI applications in manufacturing cyber–physical systems, with a particular focus on the role, maturity, and industrial readiness of emergent AI models. Following the PRISMA 2020 guidelines, a structured search was carried out in Scopus andWeb of Science, producing over 4200 publications, out of which a final set of 172 publications were retained following a rigorous multi-stage screening and eligibility process. We analysed the selected literature through complementary descriptive, longitudinal, and mapping syntheses to identify publication trends, paradigm evolution, and relationships between AI paradigms and manufacturing functions. Our findings show a clear transition from rule-based and conventional machine learning approaches toward more adaptive, decentralized, and learning-driven AI paradigms. However, despite their conceptual suitability for complex and dynamic manufacturing environments, emergent AI models are mostly limited to experimental, hybrid, or decision-support contexts, with limited integration into core manufacturing operations. Critical research gaps regarding the industrial readiness of these models—specifically concerning integration frameworks, empirical validation, safety, and trust—are identified. Furthermore, the study outlines future research directions for advancing the next generation of intelligent and autonomous manufacturing CPS. Overall, this review underscores the rapid growth and current fragmentation of the field, highlighting the need for more integrative and production-ready AI frameworks in the evolution of manufacturing CPS.