This paper presents a prescriptive microservice-based architecture that operationalizes the Reference Architectural Model for Industry 4.0 (RAMI4.0) by mapping its layered functionalities to deployable microservices. The Asset Administration Shell (AAS) is adopted as the canonical data model and interface, enabling standardized interoperability and modular scalability across heterogeneous assets. To address inconsistent adoption of reference models and integration of diverse components, the architecture combines AAS-based Submodels with MQTT publish-subscribe and HTTP-REST communication protocols through standardized interfaces. The system was validated in an automotive use case in which a vehicle was instantiated as an Industry 4.0 Component, enabling bidirectional synchronization between the physical and digital domains. All components were instrumented for metric collection and visualization, demonstrating low-latency operation, efficient resource utilization, and near-linear scalability as load increased. Quantitatively, the architecture achieved an adaptability index of 87.5%, temporal consistency of 564 ms (Asset to Digital Twin) and 2024 ms (Digital Twin to Asset), and 209 ms for database synchronization. The main contribution is a concrete architectural blueprint that enforces consistent RAMI4.0 adoption and demonstrates empirical behavior under load, thereby facilitating reuse across domains through asset-agnostic upper layers. The paper concludes with limitations and future work on security hardening, formal verification of distributed behaviors, and large-scale orchestration.

The evolution of Industry 4.0 demands seamless integration of intelligent assets capable of interoperating across various systems and contexts. This paper proposes an interoperability evaluation in intelligent systems structured according to the Industry 4.0 principles, namely the Reference Architecture Model Industrie 4.0 and the Asset Administration Shell as a digital representation standard. Two intelligent system architectures were developed and compared: a centralized approach emphasizing data interoperability and a federated learning setup demonstrating model interoperability. Both scenarios were validated using a realistic predictive maintenance dataset, and interoperability was assessed through metrics related to model performance, computational cost, and data quality. Semantic interoperability was also qualitatively analyzed, highlighting the importance of standardized data dictionaries. The results show that while the centralized model achieved higher predictive performance, the federated approach provided computational efficiency and data privacy without loss of interoperability. The study also highlights the impact of technology-related constraints, such as limitations of the machine learning framework, on the outcomes of interoperability. These findings demonstrate the viability of AAS-based architectures for developing intelligent, interoperable Industry 4.0 systems.

The pursuit of sustainability in industry requires assessing environmental impacts throughout the product life cycle (PLC). Life cycle assessment (LCA) and circular economy (CE) are essential tools for this purpose. However, monitoring and evaluating assets across all life cycle phases remains a challenge. Digital twins have emerged as a promising approach to support sustainability goals, yet there is still no consolidated architecture that integrates digital twins with sustainability metrics in a structured and scalable way. This paper presents a systematic literature review to investigate definitions of sustainability, how it can be measured, and how digital twins have been applied in this context. The findings confirm a growing trend toward leveraging digital technologies - particularly digital twins, the internet of things, and artificial intelligence - to enhance sustainability metrics such as energy consumption, emissions, and material usage. Although relevant studies propose simulations and predictive actions based on real-time data, most contributions remain fragmented or theoretical, with limited application across the full PLC. This highlights a gap in comprehensive and integrative frameworks. The study concludes by outlining the need for a robust digital twin architecture that operationalizes sustainability assessment across all PLC stages. Limitations related to database coverage and conceptual heterogeneity were recognized, and the importance of future work that includes the development and validation of a digital twin architecture was reinforced.

Federated learning has emerged as a promising application technique that prioritizes privacy and communication efficiency. By enabling collaborative model development without direct data sharing, federated learning protects sensitive information and has been successfully applied in various fields, including failure prediction. This work critically analyzes the computational cost associated with federated learning models using a case study from the automotive sector. A comparison of different classification techniques reveals that while complex models like Convolutional Neural Networks achieve robust evaluation metrics, simpler models like Logistic Regression offer competitive performance with significantly lower computational costs. This suggests that simpler models can provide an optimal balance between accuracy and efficiency, making them particularly suitable for embedded and real-time systems in federated learning scenarios.

Ventricular assist devices (VADs) are designed to provide sufficient blood flow to patients with severe heart failure. Once implanted, the patient becomes dependent on the VAD, making it essential to prevent situations that could harm the patient while receiving circulatory support. VADs are classified as critical systems (CS), and adverse events (AEs) can lead to serious consequences, including hospitalization or even death. At present, patient care is provided through in-person consultations, with incidents reported via medical device reports (MDRs) to the Food and Drug Administration (FDA). However, there is no real-time monitoring of AEs or oversight of these events. In response to this gap, a system for supervising critical adverse processes in patients with implanted VADs (SCVAD) is proposed, based on horizontally and vertically integrated architecture. This system aims to address the complexity of AEs by considering multiple domains of operation: the device, the patient, and the medical team, as well as the interactions between these entities. In this context, the formalism of Petri nets (PN) is used to develop models that represent adverse processes based on the actions recommended by the medical team. These models allow for the mapping of events with the potential to cause harm to the patient. Therefore, the medical team will be able to monitor adverse processes, as the models in interpreted PN can be isomorphically transcribed into computable algorithms that can be processed on compatible devices, enabling the tracking of complications caused by adverse processes.

Industry 4.0 (I4.0) refers to the recent changes in manufacturing technologies. In this context, intelligent equipment networks provide a high level of automation and information exchange between the elements in a manufacturing environment. On the other hand, logistics refers to the management of the flow of things (physical or digital) between a point of origin and a point of consumption, among which it includes the flow of information. Given the relevance of the information sharing on I4.0 and logistics, this work has as main goal the development of an architecture that allows the sharing of information related to the product life cycle as it moves throughout the supply chain (SC). The architecture developed is based on the Reference Architecture Model Industry 4.0 (RAMI4.0) and allows sharing the Digital Product Memory (DPM) between the SC members through Web Services. Finally, the definition of an architecture as discussed in this work addresses the consistency and interop-erability on the way that the members of a SC interact with each other.

The data-oriented paradigm has proven to be fundamental for the technological transformation process that characterizes Industry 4.0 (I4.0) so that big data and analytics is considered a technological pillar of this process. The goal of I4.0 is the implementation of the so-called Smart Factory, characterized by Intelligent Manufacturing Systems (IMS) that overcome traditional manufacturing systems in terms of efficiency, flexibility, level of integration, digitalization, and intelligence. The literature reports a series of system architecture proposals for IMS, which are primarily data driven. Many of these proposals treat data storage solutions as mere entities that support the architecture's functionalities. However, choosing which logical data model to use can significantly affect the performance of the IMS. This work identifies the advantages and disadvantages of relational (SQL) and non-relational (NoSQL) data models for I4.0, considering the nature of the data in this process. The characterization of data in the context of I4.0 is based on the five dimensions of big data and a standardized format for representing information of assets in the virtual world, the Asset Administration Shell. This work allows identifying appropriate transactional properties and logical data models according to the volume, variety, velocity, veracity, and value of the data. In this way, it is possible to describe the suitability of relational and NoSQL databases for different scenarios within I4.0.

A Indústria 4.0 (I4.0) é uma expressão usada para explicar a evolução do processo de transformação tecnológica que é observado nos sistemas produtivos, logísticos e nos modelos de negócios desde a última década. Os profissionais envolvidos utilizam a I4.0 para justificar o estabelecimento de diretrizes para este processo de transformação tecnológica, que seria caracterizada pela integração de tecnologias existentes e que foram desenvolvidas de modo relativamente independentes entre si. Para tanto, do ponto de vista de arquitetura de sistemas, foi introduzido em 2015 o Modelo de Arquitetura de Referência da Indústria 4.0 (RAMI 4.0). Este modelo de arquitetura, orientada a serviços, foi elaborado para guiar a implantação da I4.0 nas empresas do setor de modo que estas possam desenvolver suas próprias soluções (arquiteturas de sistemas), mas dentro de um contexto de interoperabilidade de sistemas de manufatura inteligente. Apesar destas arquiteturas objetivarem a otimização do desempenho dos processos envolvidos, observa-se que muitas delas tratam a estrutura de bancos de dados como meras entidades que dão suporte à sua implementação. Neste trabalho, é mostrado, por meio de um estudo de caso de sistema de manufatura inteligente, que a escolha do modelo de dados a ser utilizado pode afetar significativamente o desempenho de uma arquitetura. Demonstra-se também que a escolha pelo modelo mais adequado envolve trade-offs que podem ser analisados de acordo com um procedimento sistemático para suporte à tomada de decisões. Por fim, destaca-se a importância da persistência poliglota, em que as especificações da arquitetura são efetivamente atendidas por meio da utilização de múltiplos modelos de dados.

Manufacturing systems need to meet Industry 4.0 (I4.0) guidelines to deal with uncertainty in scenarios of turbulent demand for products. The engineering concepts to define the service's resources to manufacture the products will be more flexible, ensuring the possibility of re-planning in operation. These can follow the engineering paradigm based on capabilities. The virtualization of industry components and assets achieves the RAMI 4.0 guidelines and (I4.0C), which describes the Asset Administration Shell (AAS). However, AAS are passive components that provide information about I4.0 assets. The proposal of specific paradigms is exposed for managing these components, as is the case of multi-agent systems (MAS) that attribute intelligence to objects. The implementation of resource coalitions with evolutionary architectures (EAS) applies cooperation and capabilities' association. Therefore, this work focuses on designing a method for modeling the asset administration shell (AAS) as virtual elements orchestrating intelligent agents (MAS) that attribute cooperation and negotiation through contracts to coalitions based on the engineering capabilities concept. The systematic method suggested in this work is partitioned for the composition of objects, AAS elements, and activities that guarantee the relationship between entities. Finally, Production Flow Schema (PFS) refinements are applied to generate the final Petri net models (PN) and validate them with Snoopy simulations. The results achieved demonstrate the validation of the procedure, eliminating interlocking and enabling liveliness to integrate elements' behavior.

The fourth industrial revolution has driven initiatives worldwide following the Industry 4.0 (I4.0) context, requiring better integration and relationship between elements. This work was applied the Reference Architecture Model Industry 4.0 (RAMI 4.0) to present new models for standardizing entities in the I4.0 context to migrate legacy systems and standardize assets based on I4.0 Components (I4.0C). The management and orchestration of I4.0C can be achieved by attaching Artificial Intelligence (AI) concepts through intelligent entities describing the behavior and resources relationship, applying Multi-Agent systems (MAS), and adding self-organization, reconfiguration, plug ability, adaptation, and reasoning. Therefore, a manufacturing systems control framework is proposed based on capabilities and HMS/EPS application to orchestrate I4.0C.

This work proposes a framework for synthesis of safety-related control design in the process industries based on two aspects: (i) critical faults prevention and mitigation and, (ii) pathogenic accidents. The approaches found in the literature are based on the development of control solutions that use the results of hazard identification and risk analysis that were carried out to specify safety requirements to avoid or mitigate critical faults. However, the possibility of occurrence of the undesirable unobserved and/or hidden hazardous events associated with possible pathogenic accidents is not addressed. The objective of this work is to integrate the issue of analysis of pathogenic accidents into the context of synthesis of safety-related control design. Thus, a framework is proposed to: (1) address the issue of pathogenic accidents, which according to the analysis of accident investigation reports, its represent critical and / or undesirable unobserved and/or hidden events during the process of events evolution. This step is based on the analysis of the databases with missing data or incompleted obtained through accident records; (2) proposes an improvement in the hazard identification process, as it considers a systematic integration between the knowledge from experts (eg, automated HAZOP) and accident models that describe the critical and / or unwanted process of events evolution ensuring the principles of defense-in-depth and safe diagnosability; (3) address the use of safety barrier diagrams formalism to design a controlled degeneration process that will be treated locally, by each defense mechanism (eg, prevention / mitigation safety barrier), reducing the damage of the whole process; and (4) address the modeling, analysis and validation of defense algorithms with a focus on the prevention and mitigation of critical faults given a particular critical scenario using a hierarchical control structure based-approach via Petri nets formalism. Finally, the proposed framework is aligned with the requirements of the IEC 61511(2016) and IEC 61508(2010) Standards; and the models generated in Petri nets that have been validated, can be transcribed in an isomorphic way in control programs recommended by the IEC 61131-3 standard. The framework proposed was applied into an application example of an accident that occurred at isomerization unit of the British Petroleum (BP) refinery in Texas - USA.

Traditional manufacturing systems implement paradigms to insert more flexibility and reconfigurability in their control structure. However, these concepts deliver flexibility from reconfiguring components to meet new demand at a higher granularity level. In these systems are specified control mechanisms with predictably utilizing resource functionality. The context of Industry 4.0 reveals environments of uncertainty and greater complexity, requiring proposals for emerging paradigms, such as Evolvable Assembly Systems (EAS), that treat modular components. On the other hand, Component I4.0 (I4.0C) standardizes resources in the virtual environment, especially when it comes to the coalition of functionalities, as they are created, organized throughout the life cycle of I4.0C. This work presents an architecture proposal to deal with these systems and mechanisms for self-organization of functionalities in manufacturing systems in the I4.0 context.

In an Industry 4.0 (I4.0) context there is significant increase in information exchange and storage through the interaction among assets (machines, systems, and people). These data are important because it can lead to the autonomy of assets in decision making. However, the entire organization of I4.0 assets in terms of the quantity and the quality of information to be managed makes the system very complex. Thus, a systematic is needed to deal with this complexity where reference architectures can be used to identify the functionality required to handle this large amount and diversity of data, and how they can be organized. Therefore, the aim here is the specification of a big data acquisition process for its implementation within I4.0 context to ensure quality data for analysis and decision making. The proposed solution is based on reference architectures NBDRA and RAMI 4.0.

World statistics data confirm that cardiovascular diseases (CVDs) are the leading cause of mortality globally. CVDs include several cardiovascular system dysfunctions that affect heart functionality, such as heart failure and cardiac arrhythmias caused by coronary artery disease (CAD). The early diagnosis and prognosis of CVDs could decrease its high mortality rate. Therefore, several cardiovascular clinical assessment tools have been proposed to understand cardiovascular hemodynamics in normal and abnormal conditions. In this work, from the cardiovascular system model, which is based on the Differential Hybrid Petri Net formalism, numerical simulations for cases of healthy behavior and with CADs are performed. The Differential Hybrid Petri Net is introduced due its intrinsic feature to describe and manipulate continuous variables and discrete events that occur in the cardiovascular system. Then, the results are analyzed based on hemodynamic parameters and on the electrocardiogram for the diagnosis of CADs, in addition to comparing them with related works and clinical data, which validate the results of this study.

In the current digital era, the physical world has a cyber-representation. Both the real and virtual worlds are connected in areas, such as informatics and manufacturing. This phenomenon is currently emerging in health applications. Health 4.0 (H4.0) refers to a group of initiatives aiming to improve medical care for patients, change the business practices of hospitals and create new insight for researchers and medical device suppliers. Increasing collaboration in terms of physicians, medical equipment, artificial organs, and biosensors is a way to facilitate H4.0. In this work, a reference architecture model for Industry 4.0 (RAMI 4.0) is used to support such collaboration in medical devices control system projects. A methodology was applied to design a ventricular assist device. The results show that RAMI 4.0 is an adequate technique for modeling a device as a H4.0 component. Future works should implement the designed control system.

A Indústria 4.0 (I4.0) é uma referência para uma revolução industrial baseada em novos paradigmas produtivos. Os novos paradigmas consideram a conectividade entre todos os ativos (equipamentos, máquinas, sensores, pessoas, sistemas), aumentando exponencialmente a quantidade de dados gerados, transmitidos e processados o que incrementa ainda mais a complexidade dos sistemas. Assim, a especificação de sistemas associados a esse ambiente de big data em especial no âmbito da I4.0 não é trivial e demanda novas soluções. Considerando que a coleta de dados é uma das atividades fundamentais desse sistema, apresenta-se aqui a modelagem e análise de uma arquitetura de um sistema de aquisição de big data. O estudo é baseado a técnica do PFS / PN (Production Flow Schema/Petri net), que conta com um formalismo adequado para descrever as funcionalidades do sistema, seus componentes e as relações entre estes.