Keywords: Optimization Heuristics and Methods, Industry Applications, Resource Efficienty & Circular Economy Tracking
Abstract: Road transportation is essential to the Brazilian economy, responsible for moving approximately 60% of the country's cargo and representing 6% of the national GDP. However, the selection of trucks for this sector is a complex challenge, involving multiple criteria such as total cost of ownership, energy efficiency, delivery reliability, and sustainable practices. This article applies a systematic approach using the hybrid MEREC WISP-S method, which combines multicriteria and computational techniques to prioritize criteria and reduce subjectivity in decision-making. The study demonstrates the effectiveness of the method in analyzing suppliers and trucks, offering a replicable solution for the logistics industry. Furthermore, the results highlight the importance of criteria such as energy efficiency and sustainability. The work contributes to the integration between academia and industry, promoting innovation and resource optimization in road transportation. Future research may explore the application of the method in other industrial contexts and its integration with automation and IoT systems.

Keywords: Renewable Energy
Abstract: This work proposes the development of energy sustainability alternatives for the municipality of Conchal-SP, using the OpenDSS software as a tool for modeling, simulation, and analysis of the local electric distribution network under the concession of Neoenergia Elektro. The implementation of distributed generation scenarios, focusing on photovoltaic systems installed in public facilities to supply energy for public building consumer class, and centralized photovoltaic systems to supply energy for public lighting and public services (water and sewage treatment) consumer classes, allowed for the initial assessment of the technical, economic, and environmental impacts of the initiative. The study highlights the importance of replicable framework with methodologies for strategic sustainable energy planning in small and medium-sized municipalities, and the usage of open tools and public access databases, as a first step, together with energy efficiency practices, towards the concept of sustainable, smart and resilient cities, thus contributing to the sustainable development goals.

Keywords: Electrical Vehicle and Energy Storage, Optimization Heuristics and Methods, Power Quality
Abstract: This study evaluates the impact of battery swap stations (BSS) on distribution systems, focusing on voltage levels, losses, and costs while maintaining mobility interests. Monte Carlo simulation identifies critical scenarios post-BSS installation. An Evolutionary Particle Swarm Optimization (EPSO) method schedules battery swapping to minimize costs for swapping and distribution system transactions, complemented by a rule-based control scheme to manage daily imbalances. The algorithm supports mobility goals, addressing both scheduled and walk-in users. A case study on a 33-bus test system utilized load curves from the Federal University of Santa Maria and probabilistic data to represent swapping demand. Results show that BSS installation may cause critical issues related to costs, voltage, and losses. However, appropriate allocation and scheduling of BSS can effectively mitigate these impacts, highlighting the significance of this study in ensuring efficiency.

Keywords: Life Support Systems & Techniques, Industry 4.0, Internet of Things
Abstract: This paper presents the development of an integrated system for hemodynamic simulation of blood flow in coronary arteries, using real physiological data obtained from a G-Tech Gp400 digital blood pressure device and a pulse oximeter. Set in the context of Health 4.0, the study highlights the importance of using technologies to monitor vital signs to support blood flow analysis and clinical decision-making. The simulation was carried out with OpenFOAM, using the Navier-Stokes equations and considering the non-linear behavior of blood through a rheological model. The input data came from blood pressure sensors, while the oximeter acted as an auxiliary tool to validate the heart rate and oxygen saturation values. In addition to the simulation, an interactive web monitor was developed, implemented with Flask, HTML and CSS, capable of graphically displaying the simulated parameters and physiological signals in real time. The system operates without the need for an internet connection, which makes it suitable for environments with limited infrastructure. The results obtained were consistent with the expected physiological behavior of blood flow in a healthy adult, demonstrating the viability of the solution as a non-invasive clinical support tool. The system allows the visualization of variations in blood pressure, velocity and viscosity, making it useful for arterial stenosis studies and supporting medical and educational analysis.

Keywords: Power and Eneergy Systems, Artificial Intelligence, Power Electronics
Abstract: O constante crescimento da demanda por energia elétrica tem impactado significativamente as condições de operação dos sistemas elétricos de potência ao redor do mundo, fazendo com que operem cada vez mais próximos de seus limites máximos. Diante desse cenário, torna-se fundamental estimar a proximidade do ponto de colapso de tensão, a fim de garantir que o sistema permaneça dentro dos níveis adequados de operação. A instabilidade de tensão, definida pela queda progressiva das tensões nas barras do sistema elétrico, configura-se como um dos principais desafios à estabilidade operacional desses sistemas. Em geral, sua origem está relacionada à incapacidade do sistema em suprir a crescente demanda por potência reativa. Nesse cenário, o presente artigo propõe uma metodologia para determinar um índice capaz de refletir o nível de estabilidade de tensão do sistema, integrando esse indicador ao uso de redes neurais artificiais, com vistas à sua aplicação em análises em tempo real. O trabalho tem início com uma revisão bibliográfica que aborda os principais conceitos relacionados à instabilidade de tensão e às redes neurais artificiais. Na sequência, detalha-se a metodologia empregada para calcular o índice de estabilidade e treinar a rede neural. Por fim, apresentam-se os resultados obtidos, destacando o potencial de aplicação prática da abordagem proposta.

Keywords: Virtualization, Simulation Techniques and Augmented Reality, Internet of Things, Industry 4.0
Abstract: Developing firmware for embedded systems depends on access to the real hardware, creating barriers to consistent development, making testing slow, expensive or impossible during initial phases. Software like Renode, an open-source simulation framework, provides a full-system emulation of microcontroller-based platforms, allowing the firmware binaries to be tested in virtual environments. It also supports simulation of peripherals without the need for physical devices, debugging and scripting capabilities for automated and repeatable tests. This paper presents a simulation setup for an STM32L496 microcontroller integrated with the AT86RF233 IEEE 802.15.4 radio, enabling realistic firmware testing for wireless embedded systems. As Renode lacked native support for STM32L496, key peripherals including, RCC, GPIO, SPI, EXTI, and UART were implemented based on the official reference manual. The AT86RF233 model was also improved to simulate SPI transfers, IRQ handling, and CRC validation. The created platform runs STM32 firmware without modification, transmits and receives radio frames and log peripheral activity. The simulation setup mitigates need of early hardware acquisition, accelerates firmware development, and provides a reusable simulation baseline for Internet of Things (IoT applications).

Keywords: Industry 4.0, Automation and Process Control, Industry Applications
Abstract: This article is based on showing how the literature on Industry 4.0 and the Footwear Industry behaves, a traditional industry around the world, but with characteristics of using labor without great qualifications and simple production processes. The research is exploratory and bibliographic and carries out bibliometric research on the two subjects over the last 4 years in the web of Science database, using the Vosviewer software. Finally, it compares possible spaces for future research comparing the footwear sector of Cariri Cearense based on the work of TAVARES (2017), some articles about the region and the sector and secondary data. The article allows a diagnosis of the importance of the topic for the industry in general and points out some strategies for the footwear industry in Cariri.

Keywords: Industry Applications, Power Quality, Optimization Heuristics and Methods
Abstract: This paper introduces a methodology for planning the expansion of electrical distribution networks with an emphasis on improving reliability indicators and maintaining power quality for special industrial customers. The proposed approach uses a multi-stage genetic algorithm to optimize the investment cost, electrical losses, and financial penalties associated with inadequate service continuity. The methodology incorporates constraints related to radiality, isolation, and demand fulfillment, and it integrates short-circuit and power flow analyses to evaluate technical feasibility. Critical industrial loads that require higher levels of service reliability and voltage stability are included in the fitness function. Several expansion structures are evaluated and compared, including compact radial topologies, meshed configurations, and hybrid models with strategic reinforcements. The results show that the inclusion of reliability-oriented planning and targeted investments for critical industrial customers leads to significant improvements in the individual reliability indices, as well as reductions in overall system power losses. The proposed methodology offers a flexible and robust framework for utilities aiming to enhance the resilience of distribution systems under growing industrial demand.

Keywords: Autonomous Vehicles and Drones, Robotics and Mechatronics, Automation and Process Control
Abstract: Multi-UAV systems have recently gained attention for their ability to perform tasks with collaborative efforts compared to single UAV systems. Advancements in technology and communication devices and protocols have led to the use of UAV systems in various sectors, including defense, disaster management, and logistics missions. The increasing potential for multi-UAV applications has spurred the exploration of distributed processing to enhance communication and computation within the swarm. Along with its wide use, several challenges have also been identified for the operation of these systems in terms of battery life and computing capabilities. Several studies have explored different hardware platforms to support flight and data handling operations. In this paper, a novel system design that incorporates FPGAs in UAVs is introduced to establish a decentralized and secure wireless communication network, allowing UAVs to communicate in a peer-to-peer framework for instantaneous data exchange. By leveraging the parallel processing capability of the FPGA and the low-cost and flexible communication offered by the ESP8266 module, the proposed system helps in communicating data among the UAVs in real-time. A test case on data security is also presented. The effectiveness of the proposed study is benchmarked against state-of-the-art, and the results demonstrate the feasibility of FPGA-based networking in multi-UAV systems.

Keywords: Virtualization, Simulation Techniques and Augmented Reality, Industry 4.0, Industry Applications
Abstract: The high global demand for qualified welders, combined with the high costs and risks inherent in traditional training, favors the use of strategies such as virtual reality (VR) simulations for welding practice training. This study aims to investigate the effectiveness of the VR simulator in developing skills in Gas Metal Arc Welding (GMAW) process. Ten students with no previous experience were divided into two equal groups: one trained using VR and the other using conventional equipment. After a defined training period, performance indicators such as weld quality, productivity, time, and cost per joint were evaluated. The VR group produced five times more joints at significantly lower costs and times, achieving statistically similar weld quality. Real-time feedback and repetitive practice in a safe environment facilitated faster learning in the simulator. While some limitations were observed in specific welding techniques, the results demonstrate the feasibility of using VR as an innovative teaching strategy for initial apprenticeship training. Despite the promising outcomes, studies with larger sample sizes are recommended to confirm generalizability.

Keywords: Power Electronics, Electrical Machines and Drives, Electrical Vehicle and Energy Storage
Abstract: This paper proposes the characterization of a liquid cooled heatsink for a three-phase inverter dedicated to electric vehicle traction. Laboratory tests are carried out to characterize heatsink, the results of which will lead to obtaining a dynamic model of the heat transfer system. Comparing the heatsink temperature obtained through simulation and of the experimental results, it is possible to confirm the nearness of the thermal model developed with the real system.

Keywords: Smart Grids, Virtualization, Simulation Techniques and Augmented Reality, Industry Applications
Abstract: Data distribution is a critical aspect of power substations in Brazil, as regulatory agencies mandate control over facilities integrated into the National Interconnected System (SIN). To address this requirement, the implementation of efficient mechanisms is essential to ensure the seamless transmission of information to operation centers responsible for managing the SIN. However, discrepancies often arise between the protocols used for data acquisition and distribution, necessitating protocol conversion. This process can be executed by supervisory and control software within substations. In this context, this study proposes the implementation of a Substation Automation System (SAS) capable of acquiring field data using one protocol and distributing it via another while adhering to the minimum requirements established by the National System Operator (ONS).

Keywords: Renewable Energy, Power and Eneergy Systems
Abstract: The ever increasing demand for energy, associated with growing interest for renewable sources creates a necessity for new solutions on renewable energy. Wave energy has been a promising candidate, as an untapped and high energy density source. Despite this potential, challenges arise in the analysis for wave energy systems given their complexity. This paper proposes the use of Lagrangian mechanics to model the dynamics and energy generation of wave energy systems. The advantages of the approach are shown in a case study based on a commercial device where a model is obtained and different energy extraction rates are evaluated by changing the power take-off machinery parameters.

Keywords: Industry Applications, Industry 4.0
Abstract: This article presents an evaluation model for the digital transformation in healthcare in Brazil, promoted by the Ministry of Health (MS). The actions carried out for this purpose are guided by the document “Digital Health Strategy for Brazil 2020-2028”, ESD-2028 [1] prepared by the Brazilian Ministry of Health. The objective of this work is to present the guidelines of a model for evaluating the implementation of ESD- 2028 considering the main actions identified in said document. The method used follows the multiple location model developed at Coppe/Ufrj [2]. This model has proven to be efficient due to its ability to evaluate multiple alternatives (digital technologies) that require, for their implementation, a significant number of factors. The main information considered for the preparation of this work corresponds to the publications of the Brazilian Ministry of Health, such as the SUS Digital Program and the Monitoring and Evaluation Report of the Digital Health Strategy for Brazil 2020-2028 [3] and, to exemplify the evaluation proposal, health establishments without any identification are considered. The main results obtained by the assessment identify the digital health technologies with the best possibilities for implementation, whether in a specific health establishment or a group of establishments located in a Health region or in the country. The information generated by the assessment provides important support to planners and managers of the Brazilian health system.

Keywords: Industry Applications, Artificial Intelligence, Machine Learning
Abstract: Incentivado pela Indústria 4.0 e pelas Fábricas Inteligentes, a mineração e a siderurgia têm avançado na adoção de ferramentas analíticas, como a espectroscopia Raman, para caracterização de materiais. Contudo, a limitação no volume de dados rotulados disponíveis restringe a aplicação de modelos de aprendizado de máquina para prever parâmetros de qualidade dos materiais. Este estudo propõe o uso do modelo generativo Autoencoders Variacionais Condicionais para expandir a base de espectros Raman do coque metalúrgico produzido no processo de fabricação do aço. Os resultados evidenciaram a eficácia da metodologia em reproduzir as características principais dos espectros originais. No entanto, a predição dos parâmetros de qualidade CSR e Drum Index com os algoritmos de regressão kNN, PLS e SVR mostrou-se limitada, indicando a necessidade de pesquisas adicionais para aprimorar esses métodos na siderurgia.

Keywords: Internet of Things, Industry 4.0, Industry Applications
Abstract: This article describes the development of a solution applied in remote supervisory activities in the industrial field, using the data from a PLC (Programmable Logic Controller) and its transmission through the 4G LTE (Long Term Evolution) – CAT-M network and information publication in an MQTT (Message Queueing Telemetry Transport) server. This electronic gateway was designed based on an 8-bit microcontroller, interfacing a 4G CAT-M modem and with RS-485 Modbus RTU (Remote Terminal Unit) protocol connection. The main advantage of this solution is the link flexibility between the supervisory service at the CAT-M network coverage area. In addition, it is possible to implement this gateway in a large variety of PLCs operating with Modbus RTU protocol. The main scientific contribution is a replicable methodology to integrate legacy PLCs into IIoT systems through LTE CAT-M, offering an alternative to Wi-Fi or proprietary gateway solutions.

Keywords: Industry 4.0, Internet of Things, Automation and Process Control
Abstract: The transition from conventional automation to textit{Industry 4.0} requires the integration of Industrial Internet of Things (IIoT), cyber‑physical systems, and cloud connectivity, but also exposes Industrial Control Systems (ICS) to a rapidly expanding attack surface. A systematic literature review encompassing 126 peer‑reviewed sources and the main OT‑security standards (IEC 62443, ISO 27000, NIST 800‑82) revealed three persistent gaps: (i) limited asset visibility, (ii) protocol heterogeneity, and (iii) weak risk‑driven governance. Brazilian industry alone suffered more than 31.5 billion cyber‑attack attempts in 2021, over 20% of all incidents in Latin America. Guided by these findings, we distilled a textbf{five‑step cyber‑defence model} for ICS digitalisation that aligns Purdue levels with IEC 62443 security levels while embedding ISO 27001 controls: asset discovery, zone–conduit segmentation, CIA/ICR‑based risk assessment, standards‑oriented counter‑measures, and continuous resilience evaluation. Qualitative validation against the Stuxnet, Triton, and Industroyer attack paths indicates the model can shorten mean time‑to‑detect by 43 %. The framework offers managers and engineers a concise roadmap that harmonises compliance and risk‑based approaches, thereby enabling secure, ESG‑aware Industry 4.0 deployments.

Keywords: Electrical Machines and Drives, Automation and Process Control, Power Electronics
Abstract: O Gerador Linear a Pistão Livre (FP-PMLG) é uma opção promissora para geração distribuída de energia, destacando-se pela simplicidade mecânica, alta eficiência e compatibilidade com combustíveis alternativos. A ausência do mecanismo biela-manivela, presente em motores convencionais, exige estratégias de controle ativo para conter o deslocamento do pistão dentro dos limites operacionais adequados. Este trabalho simula uma estratégia de controle em regime permanente baseada em detecção de pico e controlador Proporcional-Integral (PI), com foco na limitação da amplitude de deslocamento do linor. As simulações, realizadas no ambiente MATLAB/Simulink®, demonstram que a abordagem é eficaz para estabilizar o sistema em movimento livre, assegurando operação segura e estável, com potencial para integração futura com técnicas de maximização da energia gerada.

Keywords: Power and Eneergy Systems, Diagnosis, Prognosis and System Identification
Abstract: Neste trabalho são analisados os impactos da degradação interna, obtida pela perda de óleo isolante e pela deterioração da estanqueidade, em transformadores para instrumentos (TI), utilizados em subestações (SE) de 230 kV. Os dados para análise foram obtidos a partir da medição de descargas parciais (DP) com 3 técnicas distintas: método elétrico normatizado, método elétrico com sensor baseado em um transformador de corrente de alta frequência (HFCT) e o método ultrassônico. A metodologia utilizada para obtenção dos resultados pretendidos compreende a realização de uma sequência de 3 ensaios de tensão aplicada (com níveis distintos de tensão) a cada retirada de 1% da massa de óleo isolante dos TI, registrando sempre a atividade de DP, simultaneamente, pelos 3 métodos sob investigação neste estudo. Os resultados comprovaram a degradação do dielétrico dos TI em virtude do aumento exponencial das DP, principalmente quando os níveis de óleo isolante baixavam além dos limites aceitáveis. Constatou-se também um aumento significativo no nível de DP quando efetuada a ação para reduzir a estanqueidade dos TIs, favorecendo a contaminação do óleo isolante com a umidade ambiente.

Keywords: Internet of Things, Industry Applications, Personalized Products
Abstract: The electric power distribution involves numerous workers who are exposed to various risks associated with operational activities. Therefore, the implementation of Internet of Things (IoT) technology is important to enhance the safety provided by Personal Protective Equipment (PPE), aiming to reduce workplace accidents. A literature review was conducted to establish a foundation for the development of a structured questionnaire directed at professionals within the power sector, in order to collect data and opinions regarding the feasibility of implementing this technology. Through this questionnaire, 37 individuals were surveyed from a target audience of 80 users, according to criteria approved by the Research Ethics Committee. This sample size was determined based on a statistical analysis to ensure representativeness and reliability. The results indicate that a significant portion of the participants from the electric power sector believe that IoT, through wearable technologies, will contribute to the development of more effective PPE, thereby increasing worker protection. Additionally, the study identified which PPE items were perceived by the respondents to be most effective when integrated with wearable sensors, based on scores derived from a Likert-scale questionnaire.

Keywords: Automation and Process Control, Internet of Things, Deep Learning and Machine Learning
Abstract: Este artigo tem como objetivo analisar os principais modelos de maturidade digital na saúde. Para isso, realizou-se uma revisão narrativa da literatura na base Scopus, resultando em 12 artigos selecionados conforme critérios de relevância e rigor metodológico. Destaca-se a inclusão de novas tecnologias, como Inteligência Artificial e Gêmeos Digitais, como tendências emergentes. A maturidade digital é abordada majoritariamente em contextos hospitalares e sistemas públicos de saúde. Conclui-se que o campo se encontra em desenvolvimento, com espaço para consolidação de modelos e aplicação em novos contextos.

Keywords: Electrical Vehicle and Energy Storage, Renewable Energy, Power and Eneergy Systems
Abstract: Este artigo apresenta um estudo teórico baseado em fatos e cálculos, a fim de apresentar uma alternativa ambientalmente limpa ao carregamento de veículos elétricos. O objetivo final desse trabalho é viabilizar o estudo proposto de um eletroposto autônomo utilizando inversores híbridos e baterias para recarga e sua replicação em ambiente acadêmico. Além disso, testes de carga, simulações e validações do modelo proposto também serão apresentadas nesse trabalho.

Keywords: Autonomous Vehicles and Drones, Robotics and Mechatronics, Automation and Process Control
Abstract: In this case study, the design and analysis of the attitude control of a rocket based on the Brazilian satellite launch vehicle known as VLS-1 is presented. The simulation model used was of 16th-order, asymmetrical, with coupled dynamics, atmospheric disturbances, actuator saturation, time delay, and bending modes which can be associated with the failure of the VLS-1 that occurred in 1999 when the engine of its second stage exploded after ignition. In this work, we assessed the effects of load disturbances and noises that would strongly interfere with trajectory dispersion, and which might be related to the intense vibratory regime occurring during the maximum dynamic pressure phase of the atmospheric flight of the launcher. The studied control algorithm was a functional digital approximation of the control system reported as the one used in the real VLS-1. The main results were that the control system failed to guarantee stability when simulated in an intense vibratory regime. The theoretical foundation to support the results was that the relative stability margins were smaller than expected in previous designs reported in the literature.

Keywords: Renewable Energy, Power and Eneergy Systems, Power Electronics
Abstract: This paper proposes the modeling, simulation, and dynamic analysis of an onshore wind farm equipped with DFIG (Doubly-Fed Induction Generator) type generators, connected to a subtransmission system. The investigation includes a detailed mathematical model of the wind farm and the subtransmission system, considering critical scenarios such as load rejection and variations in wind speed. The strategy employed for controlling each DFIG generator allows independent management of active and reactive power, optimizing electrical generation and enhancing interaction with the connected grid. The simulations are conducted using PSCAD/EMTDC software, analyzing the dynamic behavior of the system under different operating conditions.

Keywords: Industry 4.0, Industry Applications, Diagnosis, Prognosis and System Identification
Abstract: Ensuring soldering quality is critical in modern electronics manufacturing, particularly for advanced package-on-package (PoP) assemblies, where precise flux control directly influences joint reliability and component alignment. Conventional manual inspections, performed sporadically throughout production shifts, are insufficient to detect critical fluctuations in flux quantity, potentially leading to solderability defects and unscheduled downtimes. This study proposes a real-time monitoring system based on computer vision techniques using the OpenCV library to continuously evaluate the flux level at the dipping unit of pick-and-place machines. Images were preprocessed, and the flux fill rate within a Region of Interest was analyzed automatically. The proposed system was validated over a one-week industrial evaluation by performing 3578 inspections. The results demonstrated a classification accuracy of 98.21 percent, false failure rate of 1.79 percent, and Cohen’s Kappa coefficient of 0.865, indicating excellent predictive agreement. By enabling the early detection of flux shortages and minimizing reliance on manual interventions, the system significantly enhances the reliability of the soldering process and the overall manufacturing quality of PoP assemblies

Keywords: Renewable Energy, Power and Eneergy Systems, Smart Grids
Abstract: The increasing penetration of renewable energy sources, particularly wind power, poses significant challenges to power system operation and control due to their intermittent and weather-dependent nature. This paper investigates the integration of two wind power plants and a Battery Energy Storage System into the IEEE 30-bus system, focusing on enhancing voltage stability and mitigating transmission line congestion. A 24-hour dynamic load profile, based on real demand patterns and real wind power curves, has been applied to simulate daily operational behavior and evaluate the temporal impacts of renewable integration. The investigation uses MATPOWER for optimal power flow analysis and applies the Power Transfer Distribution Factor and Fast Voltage Stability Index as key metrics for BESS placement. Simulation results show that the combined deployment of wind generation and strategically located BESS significantly improves voltage profiles and reduces congestion along critical transmission paths. These findings provide practical insights for designing resilient and adaptive grid infrastructures in the context of increasing RES integration.

Keywords: Industry Applications
Abstract: This study analyzes the impact of implementing an Environmental Management System (EMS) aligned with ISO 14000 in a multinational textile plant in Brazil. Focusing on the reduction of greenhouse gas emissions and increased waste recycling, the research combines a theoretical review and a case study conducted between 2023 and 2024. Results show a 10.7%–24.2% drop in CO2 emissions and a rise in recycling rates from 65.1% to 83%, despite a 2% rise in operational costs. The EMS proved effective in enhancing environmental sustainability without compromising project budgets, supporting sustainable development in the textile sector

Keywords: Renewable Energy, Power Quality, Resource Efficienty & Circular Economy Tracking
Abstract: With the approval of the new regulatory framework for basic sanitation, which provides for contracts with performance targets that aim to increase efficiency, quality and ensure the universalization of services, as well as the United Nations Sustainable Development Goal number seven, which establishes the universalization of access to energy, through the inclusion of renewable energies, This work aims to evaluate the main technologies aimed at energy transition and energy efficiency that are being implemented in the basic sanitation sector in Brazil. In the national scenario, the sanitation sector showed a 40% increase in electricity consumption during the 1st half of 2023 to the 1st half of 2024. The increase in demand for energy contributes to the increase in greenhouse gas emissions, where Brazil is the sixth largest emitter in the world. In this aspect, the methodology applied was bibliographic and documentary research, where large service providers were selected for qualitative and quantitative analysis of the data and information collected. The results showed that service providers are adopting the main technologies aimed at energy efficiency in their operating units. However, they did not present significant values of performance indices in relation to expenses and consumption of electricity in water and sewage systems.

Keywords: Industry Applications, Artificial Intelligence, Internet of Things
Abstract: Este artigo de revisão sistemática investiga as principais tecnologias digitais adotadas na prática clínica no Brasil e as evidências que sustentam sua utilização e impacto. Os resultados revelaram um aumento significativo nas publicações sobre o tema em 2023 e 2024, com destaque para as técnicas de Machine Learning (ML) e Deep Learning (DL). As tecnologias foram agrupadas em cinco áreas principais: Atenção Primária e Telemedicina, IA/ML em Diagnóstico e Oncologia, IA/ML em Áreas Diversas, Big Data e Infraestrutura de Dados, e Aplicações Específicas/Emergentes. Uma síntese identificou quatro áreas centrais de aplicação: Telemedicina e Saúde Digital, Machine Learning e IA em Saúde, Big Data em Saúde e Processamento de Sinal em Grafos e Aprendizado em Grafos. A discussão aponta para uma convergência no reconhecimento do potencial transformador das tecnologias digitais, especialmente impulsionado pela pandemia de COVID-19, e na identificação de barreiras significativas para a adoção equitativa, como a exclusão digital e desafios de infraestrutura, regulamentação e ética. A qualidade dos dados é ressaltada como crucial para o sucesso das aplicações de IA/ML. Em conclusão, o estudo delineia o crescente interesse e aplicação de tecnologias digitais na saúde brasileira, com a telemedicina e a IA/ML demonstrando grande potencial para melhorar o acesso e a qualidade do cuidado. No entanto, a superação das barreiras existentes e a integração sustentável dessas tecnologias nas políticas de saúde, especialmente na APS, são consideradas fundamentais para uma adoção ampla e justa. A pesquisa sugere futuras investigações focadas nas experiências de pacientes e profissionais para uma implementação mais centrada no usuário.

Keywords: Power Quality, Personalized Products, Industry Applications
Abstract: This systematic review approaches the dielectric breakdown voltage in gases from a physical-fundamental and metrological perspective, highlighting industrial applications. Based on a consolidated corpus of the Web of Science, IEEE Xplore, and Scopus (2021–2025) databases, studies on emission and ionization mechanisms, environmental effects (pressure, temperature, humidity), and geometric factors (micro/nanogaps, field non-uniformity) that modulate breakdown voltage and Partial Discharge Inception Voltage (PDIV) were analyzed. Recent literature points out limitations of Paschen's Law in non-ideal regimes, driving dimensionless models, simulations, and controlled trials to support calibration, uncertainty, and traceability in high-voltage measurements. Experimental evidence in cryogenics and in short heated gaps supports correction curves and environmental protocols, while studies on materials (ferroelectrides, porous dielectrics) and critical sectors (energy, aerospace, instrumentation) reinforce guidelines for qualification, purchase specifications, and interlaboratory comparability. We propose practical recommendations: (i) use of Paschen's Law; (ii) environmental and preparation protocols as part of the method; (iii) adoption of calibrated models and correction curves for safe extrapolation between laboratories.

Keywords: Smart Grids, Power and Eneergy Systems, Internet of Things
Abstract: This paper presents an open-source microgrid monitoring system designed to address the limitations of traditional solutions in research and educational settings. The proposed system combines an asynchronous Python backend for Modbus TCP data collection with a lightweight web frontend using modern standards (HTTP/3, TLS 1.3, IPv6). Key innovations include: (1) a YAML-based declarative configuration system that simplifies device integration compared to database-centric approaches; (2) phase-aware data visualization with automatic unit conversion and multi-axis scaling; (3) built-in simulation capabilities for development and testing. Implemented as a containerized solution using MongoDB, Flask, and vanilla JavaScript, the system achieves sub-second polling intervals while maintaining browser compatibility without proprietary client software. We validate the approach through comprehensive testing with simulated microgrid devices. The solution particularly benefits distributed energy research by providing customizable visualization and seamless transitions between simulated and physical devices — addressing critical needs in academic microgrid laboratories while maintaining industrial protocol compatibility.

Keywords: Diagnosis, Prognosis and System Identification, Big Data in Industry Applications, Industry Data Science Applications
Abstract: Este artigo explora as aplicações de modelos de Inteligência Artificial Generativa (IA Generativa), como GPT, DALL-E e Stable Diffusion, dentre outras, na geração de dados sintéticos e no aprimoramento de modelos de machine learning. Abordamos os desafios técnicos e éticos associados ao uso desses dados, como viés algorítmico e qualidade dos dados gerados. Por meio de um estudo de caso, demonstramos como dados sintéticos podem ser utilizados para treinar modelos em cenários com dados reais limitados. Os resultados indicam que a IA Generativa pode ser uma ferramenta poderosa para superar limitações de dados, mas requer cuidados para garantir a confiabilidade e a ética nas aplicações.

This article explores the applications of Generative Artificial Intelligence (Generative AI) models, such as GPT, DALL-E and Stable Diffusion, among others, in generating synthetic data and improving machine learning models. We address the technical and ethical challenges associated with using this data, such as algorithmic bias and quality of the data generated. Through a case study, we demonstrate how synthetic data can be used to train models in scenarios with limited real data. The results indicate that Generative AI can be a powerful tool for overcoming data limitations, but requires care to ensure reliability and ethics in applications.

Keywords: Power Electronics, Power and Eneergy Systems, Renewable Energy
Abstract: Com o crescimento da geração de energia através de módulos fotovoltaicos em casas e pequenos comércios, os microinversores têm se tornado cada vez mais populares. Dessa forma, ao realizar um estudo a respeito de microinversores do tipo buck-boost que possuem características como estágio único, ausência de transformador, aterramento comum e capaz de operar conectado à rede elétrica, uma topologia que reúne todas essas características foi identificada. A partir disso, um estudo mais aprofundado foi realizado nessa topologia. No presente trabalho são discutidas diferentes estratégias de modulação e seus impactos na forma da corrente de entrada, extração de potência e ganho de tensão. Uma técnica recente de modulação com dois sinais de referência apresenta melhorias, contudo nota-se a ausência de metodologia e de projetos de controle em malha fechada detalhados na literatura, consequentemente os resultados também deixam a desejar. Desse modo, propõe-se a investigação de estratégias de controle em malha fechada e validação experimental em trabalhos futuros.

Keywords: Electrical Machines and Drives, Industry Applications
Abstract: Despite their known advantages for power factor control and electromechanical stability, synchronous machines without damper windings face significant startup challenges, primarily related to transient currents and torque oscillations. This paper presents an experimental and analytical study of the transient electrical behavior during the startup of an 8 kVA synchronous alternator coupled to a 1 HP induction pony motor, analyzing distinct conditions of excitation and speeds. Two assisted-start methods, direct-on-line (DOL) and inverter-assisted startup, were compared. Experimental evaluations were conducted on a custom-built test bench with manual synchronization and high-resolution data acquisition. Complementary simulations performed in Simulink provided insights into electromagnetic torque profiles that were not directly measurable on the bench. Results demonstrated a significant reduction exceeding 50% in armature current peaks and substantial attenuation of torque oscillations when employing inverter-assisted startup. Additionally, the study validated the relationship between the V-curve minimum and optimal synchronization conditions, emphasizing the correlation between excitation levels, minimal armature current, and reduced electromechanical stresses. These findings underscore the effectiveness of pony motors as a cost-efficient and reliable solution for medium-power synchronous machines, highlighting inverter assistance as a critical enhancement for systems where mechanical integrity and reliable grid synchronization are paramount.

Keywords: Industry Data Science Applications, Deep Learning and Machine Learning, Diagnosis, Prognosis and System Identification
Abstract: In industrial rotating machinery, early detection and classification of mechanical faults are critical for ensuring operational safety and minimizing unplanned downtime. This study presents a data-driven condition monitoring framework utilizing multi-modal sensor data to detect rotor imbalance and infer likely fault categories in a radial fan system. The proposed approach leverages Dynamic Time Warping (DTW) to quantify the deviation of current operating conditions from a healthy baseline, using a MinMax-scaled DTW distance to generate an interpretable anomaly score ranging from 0 to 100. By mapping score intervals to standardized fault categories, the system not only detects anomalies but also provides fault interpretability grounded in vibration analysis theory. Experimental validation is conducted on a dataset containing a known imbalance fault, and the results demonstrate that the proposed method accurately identifies the fault type while showing strong temporal alignment with existing fault metrics. The integration of unsupervised anomaly score estimation with fault severity classification charts offers a practical and scalable solution for predictive maintenance in real-world industrial settings.

Keywords: Electrical Vehicle and Energy Storage
Abstract: The global mobility landscape has undergone significant transformation due to growing environmental concerns, particularly regarding the emission of air pollutants. In this context, electric vehicles (EVs) have increasingly gained market share, raising new concerns about their safety—especially in terms of fire hazard. This study aims to conduct a comparative analysis of the incidence of fires in electric vehicles and those with internal combustion engines (ICE), in order to assess which technology demonstrates greater reliability from a fire safety perspective. To achieve this, data were collected from reliable sources such as governmental reports and peer-reviewed scientific articles, focusing on countries with a significant share of electrified fleets. The methodology involved normalizing the data based on each country’s active fleet, enabling a proportional and comparative analysis of fire incidence. Results indicate that, in relation to the circulating fleet, electric vehicles exhibit a ratio of fire approximately ten times lower than that of ICE vehicles. However, limitations in data availability and standardization were also observed, which may affect the accuracy of some findings. It is concluded that although electric vehicles statistically present greater safety in terms of fire occurrence, further improvements in data systematization and transparency are essential for future research to provide more robust and reliable evaluations of vehicle safety.

Keywords: Industry Applications, Life Support Systems & Techniques, Personalized Products
Abstract: Heart failure (HF) is one of the world’s most frequently diagnosed cardiovascular diseases. An important element of heart failure management is cardiac rehabilitation, the goal of which is to improve patients’ recovery, functional capacity and health-related quality of life. Patients with HF in cardiac rehabilitation may lack sufficient motivation or may feel that the rehabilitation process does not meet their individual needs. One solution to these challenges is the use of various technologies with the potential to support the cardiac rehabilitation process, which have emerged primarily in the post-pandemic period. This article presents a scoping review of the main technologies that can support the cardiac rehabilitation process for patients with heart failure from 2019 to 2024. Screening more 172 articles from 4 databases, 59 full-text articles were selected to collect data and insights. The review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for scoping reviews) guidelines. The reviewed studies demonstrated the application of various digital technologies, including web-based platforms, mobile applications, artificial intelligence and machine learning, wearable devices, telehealth, assistive robots, smart hydration bottle, virtual reality and digital games, as well as virtual assistants. Overall, the findings indicate that the use of these technologies is comparable to, and in some cases superior to, traditional rehabilitation programs in terms of patient adherence and motivation among individuals with HF, particularly those living in remote areas. These findings are expected to inspire future research on accessible, personalized interventions and foster the use of emerging technologies in heart failure care.

Keywords: Renewable Energy, Power and Eneergy Systems, Smart Grids
Abstract: This paper comprehensively evaluates how different frequency estimation techniques affect the performance and Non-Detection Zones (NDZs) of anti-islanding protection systems in distribution grids with inverter-based resources (IBRs). Motivated by the growing integration of distributed generation and the requirements of IEEE Std. 1547-2018, the study focuses on passive (under/overfrequency and ROCOF) and active islanding detection methods. Three frequency estimation algorithms are analyzed: Zero-Crossing (ZC) detection, Synchronous Reference Frame Phase-Locked Loop (PLL-SRF), and Discrete Fourier Transform (DFT). The performance of these approaches is assessed through extensive simulations in ATP, covering 2080 operating scenarios across multiple active and reactive power imbalances, totaling 6240 cases, and considering variations with and without active islanding detection methods. The results demonstrate that the choice of frequency estimator significantly influences the NDZ for ROCOF protections and that the dynamic response of each estimator interacts differently with the transient behavior induced by active IDMs. For some configurations, considerable variation in NDZ was observed, revealing the potential for non-detection events even with active IDMs, depending on estimator dynamics and protection settings. This work highlights the importance of selecting and coordinating frequency estimation methods and anti-islanding schemes in modern grids with high IBR penetration, emphasizing case-specific studies and comprehensive testing for reliable protection in practical deployments.

Keywords: Internet of Things, Virtualization, Simulation Techniques and Augmented Reality, Industry 4.0
Abstract: The teaching bench is used to teach process control classes and has an industrial automation system to control variables such as level, pressure, flow and temperature. However, currently installed devices do not have intrinsic means to generate, connect and integrate data, transforming them into relevant plant information and facilitating the visualization of this information by users (teachers and students) during learning and testing. Therefore, this article addresses the integration of three technologies that enable Industry 4.0: Industrial Internet of Things (IIoT), Industrial Augmented Reality (IAR) and Cloud Computing, with the aim of modernizing a didactic process control bench. The cloud computing application is carried out through the TagoIO cloud platform, where the following functions were used: MQTT Broker to receive IoT messages, database to store and generate a history of received variables, dashboard to present the stored data and email alerts to indicate potential problems.

Keywords: Cyber Physical Systems, Digital Twins and Knowledge Systems, Virtualization, Simulation Techniques and Augmented Reality, Robotics and Mechatronics
Abstract: Virtual Commissioning (VC) has emerged as a powerful approach for validating industrial automation systems through simulation-based environments, allowing engineers to test and debug control logic before deploying to physical systems. This paper presents the application of the Software-inthe-Loop (SiL) methodology to perform virtual commissioning of a Packaging Module, aiming to reduce commissioning time and increase system reliability. The proposed approach integrates several tools within an industrial communication network based on the OPC Unified Architecture (OPC UA) standard. Key software used includes TwinCAT 3 for Industrial Personal Computer (IPC) programming, DELMIA in 3DEXPERIENCE for 3D simulation, and UaExpert for OPC UA communication testing. The system architecture also incorporates legacy OPC DA connectivity via UaGateway and Matrikon OPC Explorer. Results demonstrate the effectiveness of SiL in enabling early-stage validation of control logic, while highlighting challenges related to interoperability across platforms. This study reinforces the potential of Virtual Commissioning as a fundamental component in the digital transformation of smart manufacturing environments.

Keywords: Automation and Process Control, Robotics and Mechatronics, Industry Applications
Abstract: This paper presents an adaptive sliding mode control (ASMC) scheme for stabilizing an underactuated double-gimbal control moment gyroscope (UDGCMG). The ASMC handles the system’s nonlinearities, compensates the parametric uncertainties via online adaptation, and enforces the actuated and unactuated position bounds through smooth saturations in the sliding manifold design. The boundedness and asymptotic convergence of the system tracking errors using the ASMC despite the constraints and disturbance is proven through Lyapunov direct method. The ASMC is compared with an adaptive linear quadratic regulator (ALQR) based control scheme previously designed for the UDGCMG using MATLAB/Simulink simulations for a UDGCMG under four scenarios: start-up from a nonzero initial tracking error (Case 0), tracking a constant reference (Case I), tracking a time-varying S-curve reference (Case II), and tracking an S-curve reference with a sinusoidal disturbance (Case III). Compared to the ALQR, the proposed ASMC reduces the root-mean-square tracking error by 95.53%, 99.99%, and 99.90%, and reduces the power consumption by 97.45%, 99.99%, and 71.96% in Cases 0, I, and II, respectively. In Case III, the ASMC improves the tracking accuracy by 67.4% at the cost of 51.4% increase of power consumption. Moreover, both the unactuated position and the actuated gimbals remain within their admissible ranges when using the ASMC, whereas exceeding the prescribed bounds when using the ALQR.

Keywords: Smart Grids, Industry 4.0, Electrical Machines and Drives
Abstract: This study focuses on examining LoRa technology, which is based on Internet of Things (IoT) tools and its integration in transforming communication networks for intelligent systems, with an initial focus on its application to electrical substations. To assess the viability of this technology, experiments are conducted in various environments, both in the field and in controlled settings. The project begins with the development of a prototype LoRaWAN wireless sensor network to monitor electrical variables. The implemented network enables the evaluation of different performance metrics, providing crucial information for future improvements in the monitoring infrastructure of electrical substations, as well as addressing the challenges of technology. The study concludes by emphasizing the potential of LoRaWAN to revolutionize the monitoring and communication infrastructure in the electric power sector. It provides key insights for optimizing the implementation of LoRaWAN technology in smart grids and suggests avenues for future research and applications within this context.

Keywords: Internet of Things, Large Scale and Network Control, Industry 4.0
Abstract: This work provides a comprehensive analysis of the OMLOX standard, exploring its architecture and applications in industrial environments, emphasizing the importance of the Industrial Internet of Things (IIoT) and Ultrawideband (UWB) technology in the current technological era, with a focus on its pivotal role within the context of OMLOX. Furthermore, a case study of UWB implementation by leading organizations such as Apple and the Car Connectivity Consortium is conducted, highlighting how this innovative technology has revolutionized the industry and enhanced efficiency and precision in location- based operations

Keywords: Power and Eneergy Systems, Renewable Energy, Smart Grids
Abstract: Power systems are prone to faults with low-current amplitudes that fail to trigger conventional overcurrent relays. Typically, high-impedance faults (HIF) cause these issues, arising from energized conductors touching surfaces with poor conductivity, insufficient to surpass the protection’s threshold. Another contributing factor is photovoltaic distributed generator equipped with low voltage ride-through, which may add to fault currents during short circuits on their connected lines. To address these challenges, this study explores a differential protection using positive sequence current components and the least squares method (LSM) to identify and isolate low-current short circuits. This approach, alongside the active distribution network (ADN), has been modeled and tested in Matlab/Simulink software. The proposed Positive Sequence Differential Protection based on the Least Squares Method (PDSP-MMQ) demonstrated effective performance in detecting low-current faults, including HIF, in both conventional and active ring-topology distribution networks. It successfully identifies fault contributions from photovoltaic distributed generators (GDFV) operating in LVRT mode and responds rapidly to various fault types, including direct short circuits and three-phase faults in non-solidly grounded systems, across a wide range of fault impedances.

Keywords: Resource Efficienty & Circular Economy Tracking
Abstract: This research analyzes the non-governmental organization Pimp My ®and its software Catak®i as representative examples of contemporary practices in social innovation, solidarity economy, and circular economy in Brazil. Using thematic analysis methodology, a qualitative case study was conducted based on interviews and institutional documents. The results reveal seven core themes: the social visibility of waste pickers, the use of social technologies, empowerment, digital inclusion, logistical challenges, networked organization, and income diversification. The analysis highlights how these initiatives foster a structural transformation in the working conditions of waste pickers, enhancing their autonomy and social recognition. The study contributes theoretically by articulating the concepts of social innovation, social technology, and circular economy. It concludes that such practices generate significant social and environmental impacts, although they still face structural limitations and depend on more consistent public policies.

Keywords: Life Support Systems & Techniques, Machine Learning, Diagnosis, Prognosis and System Identification
Abstract: Population aging has led to a significant increase in cases of dementia, requiring new approaches to their detection and management. In this context, this article aims to present the methodology of the SMILIGHT project, proposing the development, validation and testing of a new application that integrates an algorithm based on biosignals for the monitoring and management of non-pharmacological therapy for people with dementia. The study involves 75 participants and will be carried out in three phases. Data will be collected using technologies such as EEG (electroencephalogram) and EDA (electrodermal activity), allowing the analysis of patients' emotional-cognitive patterns. The system will be able to present information on personalized therapeutic prescriptions via a web application. In addition, advanced machine learning algorithms, trained to identify and classify patients' emotional-cognitive states as well as the stages of dementia, aim to personalize interventions. By integrating non-pharmacologic therapeutic innovations with advanced technologies, the SMILIGHT project adopts an efficient and sustainable approach to the care of people with dementia, as well as promoting green technology principles, which seek to minimize waste and the excessive use of resources, particularly pharmacological ones.

Keywords: Smart Grids, Power and Eneergy Systems, Renewable Energy
Abstract: In recent years, renewable energy sources have experienced significant growth within power systems. However, their high penetration levels in localized areas can lead to operational challenges such as reverse power flows in transmission lines and voltage violations along distribution networks. As a result, assessing the hosting capacity of distribution systems has become essential to ensure the safe and effective integration of renewable energy. This study presents a bibliometric analysis aimed at identifying and categorizing the optimization methods employed to evaluate hosting capacity in distribution networks. In addition, it explores the practical measures adopted to mitigate the consequences of unregulated photovoltaic integration, including advanced inverter functions such as volt-watt control. By analyzing past research contributions and mitigation efforts, the study seeks to inform future strategies that foster the development of more efficient, resilient, and sustainable electrical systems capable of supporting high levels of distributed generation.

Keywords: Autonomous Vehicles and Drones, Robotics and Mechatronics, Virtualization, Simulation Techniques and Augmented Reality
Abstract: Simuladores de veículos para mineração são essenciais na capacitação de operadores por sua segurança, baixo custo e capacidade de simular cenários complexos. No entanto, limitações espaciais impedem a reprodução fiel dos movimentos reais, especialmente em caminhões fora de estrada. Visando avaliar e melhorar a experiência de aprendizagem do operador em um sistema de simulação de caminhão fora de estrada do tipo CAT793F, este trabalho apresenta uma metodologia para implementação de um Algoritmo de Motion Cueing (MCA) do tipo washout em uma base de movimento com 2 graus de liberdade (DoF), projetada para simular movimentos de pitch e roll, adequados às condições de operação de caminhões fora de estrada em ambientes de mina. O modelo adotado foi comparado com outra abordagem e apresentou resultados promissores, destacando-se pela melhor representação do movimento, menor atraso de resposta e redução de ruídos provenientes do sistema.

Keywords: Resource Efficienty & Circular Economy Tracking, Renewable Energy
Abstract: LCA (Life Cycle Assessment) is the most complete tool for environmental impact assessment as it includes a large number of indicators related to the impact of human activity from the material extraction and procurement, manufacture, use and final destination of the product or process under evaluation. Such complexity results in the difficulty of making environmentally friendly decisions at the time of the initial design of products. This work intends to contribute to the study of an alternative and simplified proposal, adopting the amount of embodied energy as an indicator of the entropy level of the proposed system, so that there is a reduction of intensity in manufacturing from the adoption of a quantitative view in thermodynamic terms of industrial production and offering approaches to a consistent decision-making process.

Keywords: Power and Eneergy Systems, Renewable Energy, Artificial Intelligence
Abstract: A crescente demanda global por energia limpa tem impulsionado a adoção da Geração Distribuída (GD), em especial a fotovoltaica. No Brasil, a Resolução Normativa nº 1059/2023 da ANEEL, em conjunto com a Lei 14.300, consolida o marco legal para GDs com potência instalada de até 3 MW, transformando a rede de distribuição de passiva em ativa. Esse novo cenário, no entanto, insere o fluxo de potência bidirecional na rede, gerando sobretensões e prejuízos a equipamentos e concessionárias. Para minimizar tal problema, este trabalho apresenta um estudo de caso de uma rede de distribuição real, onde compara-se o perfil de tensão da rede para diferentes níveis de penetração fotovoltaica. O objetivo é avaliar o desempenho da rede quanto à capacidade de hospedagem e ocorrência de sobretensões, considerando a inserção de geração distribuída fotovoltaica combinada a sistemas de armazenamento de energia estrategicamente posicionados. Para isso, são utilizadas diferentes ferramentas computacionais, como algoritmos de aprendizado de máquina para previsão da irradiação solar, simulações de Monte Carlo para representação de incertezas operacionais, e técnicas de alocação de baterias — incluindo inteligência artificial — para avaliar o nível de tensão em pontos relevantes da rede. As simulações de fluxo de potência foram conduzidas no software OpenDSS, com controle automatizado via MATLAB. Os resultados demonstraram que, a partir de aproximadamente 50% de NP, as sobretensões tornam-se críticas, especialmente em pontos distantes do transformador. No entanto, a inserção de sistemas de armazenamento mostrou-se eficaz na mitigação desses efeitos. A estratégia com alocação iterativa resultou em uma redução de tensão de até 5,71%, enquanto a com rede neural alcançou uma redução de até 3,70%.

Keywords: Electrical Vehicle and Energy Storage, Electrical Machines and Drives, Renewable Energy
Abstract: The energy transition is a central issue in the global climate context, and the electrification of boats in the Amazon has emerged as a promising strategy to reduce the environmental impacts caused by the use of combustion engines, which are widely used in river transportation in the region. These engines are inefficient and highly polluting, affecting human health and aquatic ecosystems. Replacing them with electric motors, especially if they are powered by renewable sources, offers a clean, efficient and sustainable alternative, with the potential to preserve the environment and benefit riverside communities. This work proposes the numerical analysis of the design of a Brushless DC (BLDC) electric motor, specifically for small boats in the Amazon, using computer simulations with the Finite Element Method (FEM), with a focus on achieving stable rotation, constant torque and efficiency above 90%

Keywords: Automation and Process Control, Industry Applications, Internet of Things
Abstract: Este artigo apresenta o desenvolvimento de um sistema supervisório para automação industrial, empregando exclusivamente recursos de código aberto. A motivação para esta pesquisa reside na busca por soluções acessíveis e flexíveis para a indústria, promovendo a redução de custos e a liberdade de personalização. O trabalho descreve a seleção e integração de ferramentas de código aberto para cada etapa do desenvolvimento do sistema supervisório, incluindo a aquisição de dados, visualização em tempo real, armazenamento e análise de dados, e interface de usuário. Durante a realização do trabalho, foram utilizadas técnicas de programação para implementar a comunicação entre os dispositivos da rede, a lógica de automação e a interface de usuário web. Em meio a tendência crescente de integrar equipamentos e objetos da indústria ao conceito de Internet das Coisas, formando a então Industrial Internet of Things (IIoT), os resultados obtidos demonstram a viabilidade e eficácia da solução proposta, fornecendo uma plataforma viável para a automação industrial.

Keywords: Power Electronics, Renewable Energy, Power and Eneergy Systems
Abstract: Finding the maximum power point (MPP) of a photovoltaic (PV) system and staying close to it requires more effort, since PV panels are often subjected to partial shading. When this occurs, several local maxima appear on the P-V curve, and algorithms based on local search, such as perturb and observe (P&O) and incremental conductance (INC), are unable to reach this point. For this type of application, global maximum power point tracking (MPPT) algorithms are used, such as the particle swarm optimization (PSO) technique, but they present their difficulties. A hybrid approach using a global search technique and a local search technique solves this previous problem. This work proposes a hybrid MPPT, mixing P&O and PSO techniques, applied to a two-switch buck-boost (TSBB) DC-DC converter connected to a PV panel subject to shading conditions. The proposal presents better efficiency, convergence time and less oscillation compared to the two separately. Simulation results, using PSIM software, are presented in order to show the effectiveness of this proposal. In this case, tests are performed to demonstrate the correct functioning of the proposed algorithm.

Keywords: Industry 4.0, Artificial Intelligence, Personalized Products
Abstract: In a healthcare ecosystem, artificial intelligence (AI) enables more agile and precise interventions, improving the diagnostic process, particularly in medical emergencies, where time is a critical element in decision-making. In this context, AI mitigates human errors, improves decision-making, and results in clinical outcomes with fewer risks for patients. However, its implementation faces challenges, such as limited infrastructure, resistance to technological adoption, and ethical concerns related to data privacy. Based on the above, this research investigates the enablers and inhibitors of AI to speed up the flow of information and decisions in hospital emergencies. A multiple case study was conducted with actors linked to the emergency sector to achieve this objective. Based on the comparative analysis of the cases, we identified six inhibitors and eight facilitators for the adoption of AI in healthcare ecosystems. Strategies that overcome technical and cultural barriers are essential for effective implementation without compromising humanized care. The findings indicate that adopting AI can transform and improve the agility and accuracy of decision-making processes, especially in emergency services, such as triage and prioritization of care. The study reinforces the need for sustainable integration of AI as a transformative means to improve operational efficiency and the accuracy of care provided in AI-based emergency services.

Keywords: Power Quality, Power Electronics, Industry Applications
Abstract: The increasing use of non-linear loads, such as uninterruptible power supplies and LED lighting, has raised concerns about power quality in electrical systems, primarily due to harmonic distortion. Programmable electronic loads (PELs) are effective tools for emulating these non-linear loads in controlled environments; however, commercial solutions are often costly and inaccessible to laboratories with limited budgets. This paper presents a novel single-phase PEL architecture that integrates a non-linear sliding mode controller with an anti-windup mechanism, ensuring low tracking error, fast dynamic response, and robustness to parameter variations when compared to conventional controllers. The proposed system enables flexible generation of harmonic current profiles by defining a finite sum of the fundamental and selected harmonic components with specified amplitudes, and supports operation with or without an electric grid connection. Simulation results demonstrate that the system maintains high accuracy and robustness across various harmonic profiles—including a single low-order harmonic, a single high-order harmonic, and combinations of mixed harmonics—while preserving a low implementation cost by requiring only one power switch per phase.

Keywords: Smart Grids, Renewable Energy, Power and Eneergy Systems
Abstract: The growing demand for more resilient and sustainable energy systems has driven the adoption of microgrids integrated with renewable energy sources. However, the lack of flexible modeling methods still poses a challenge for the design and automation of such systems. This work proposes a Petri Net-based approach to graphically represent the operational states and transitions of a microgrid. As a formal method, Petri Nets provide a mathematically grounded structure for modeling dynamic behavior, enabling precise analysis and predictable control logic. The model is converted into XML format (PNML) and processed by a Raspberry Pi Pico microcontroller using MicroPython. The system’s operation was validated through MQTT communication and LabVIEW monitoring, confirming the correct execution of the modeled transitions. These results demonstrate the feasibility of the proposal as an accessible alternative to FPGA-based implementations and enable clear visualization of microgrid operational dynamics compliant with the IEC 61850 standard. Furthermore, the approach lays the groundwork for future applications based on real-time events, such as current and voltage measurements.

Keywords: Renewable Energy, Power and Eneergy Systems
Abstract: Este artigo apresenta uma análise da oportunidade de produção de hidrogênio verde no mercado brasileiro, a partir do aproveitamento de energia renovável proveniente de usinas eólicas, fotovoltaicas e hidrelétricas que possam não ser aproveitadas para gerar eletricidade devido a indisponibilidades externas, razões energéticas ou relacionadas à confiabilidade do sistema elétrico nacional. Motivado pela transição energética e pela crescente inserção de fontes renováveis na matriz elétrica brasileira, o estudo traça um panorama dos desafios enfrentados pelo setor, com destaque para as limitações no escoamento da energia, os critérios adotados para o ressarcimento de usinas por restrições de operação e as tecnologias utilizadas na produção de hidrogênio por eletrólise. Além disso, são apresentados dados sobre a quantidade de energia desperdiçada, em GWh, nos últimos anos, associando esse volume ao potencial de hidrogênio que poderia ter sido produzido e comercializado para a indústria ou exportado.

Keywords: Industry 4.0, Virtualization, Simulation Techniques and Augmented Reality, Human Machine Symbiosis
Abstract: The objective of this paper is twofold. First, we aim to synthesize, evaluate, and discuss the main applications of Motion Capture Technologies (MOCAP) in industrial environments. Based on these results, we propose a theoretical framework that combines MOCAP technologies with the Value Stream Mapping (VSM) methodology from the Lean approach. A basic proof of concept is also presented. To accomplish the research objectives, a systematic literature review (SLR) was conducted. Data were collected in the Scopus database without time restriction. A total of 41 articles were selected to provide metadata that were analyzed and further used to propose the theoretical framework. Findings provide evidence that MOCAP technologies have the potential to revolutionize parameter and data acquisition in the manufacturing environment, as they are a human-centered type of sensing solution. Main application domains so far are ergonomic assessment, workplace design, and optimization of production systems. The proposed theoretical framework aims to initiate a debate on improving production processes in the Industry 4.0 and Industry 5.0 eras by combining MOCAP technologies and Lean concepts. The main limitations of this study are related to the defined query string and the databases, as some essential studies may have been left out. Furthermore, the proposed framework is theoretical, and only a proof of concept was developed, requiring further empirical evaluation in real industrial settings.

Keywords: Electrical Machines and Drives, Industry Applications, Power Electronics
Abstract: O acionamento do motor de indução trifásico utilizando Inversor de Frequência proporciona um ajuste fino de velocidade e torque eletromagnético da máquina elétrica. Nos inversores de frequência, o controle de tais parâmetros é feito através do chaveamento de elementos semicondutores em alta frequência, chamados IGBTs (transistor bipolar de porta isolada). A saída do conversor de frequência apresenta a denominada Tensão de Modo Comum no motor de indução. Tal fenômeno é uma tensão não-nula medida entre o neutro da máquina e o terra. Esses sinais de alta frequência induzem na máquina tensões e correntes, as quais geram danos em peças vitais do motor, reduzindo sua vida útil e provocando paradas indesejadas para manutenções não programadas, o que afeta toda uma linha de produção. Esse trabalho visa a proposição do cálculo de capacitâncias parasitas em um elemento da máquina denominado mancal. O conhecimento desses parâmetros elétricos é essencial para a determinação de correntes de alta frequência que circulam pelo elemento. Para tal, será feita a modelagem via Método de Elementos Finitos utilizando o software FEMM 2D aliado à Teoria de Lubrificação Elastohidrodinâmica para o cálculo do valor da capacitância do mancal.

Keywords: Automation and Process Control
Abstract: This paper describes a simulation project of the PID cross-limit and cascade control strategy, with the support of transfer functions integrating PLC Siemens S7-1512, MATLAB and InduSoft. The adopted concept is based on the advanced technique of cross-limit control with the purpose of maintaining the mixing ratio between 2 products in a system consisting of a total of 6 transfer functions. The control system was developed through a TIA Portal V16 simulator in the Siemens S7-1512 programmable logic controller interconnected to a remote station in the Profinet network connected to the transfer functions via OPC DA Indusoft. The method chosen to tune the simulator is Internal Model Control, highlighting specific performance criteria for each process variable. The project highlights the importance of solid theoretical knowledge in transfer functions to the Laplace domain in closed loop and its application in achieving an effective performance of the process control system.

Keywords: Renewable Energy, Power Electronics, Power Quality
Abstract: This study evaluates the integration of solar, wind, and wave energy sources into the power system of a Floating Production Storage and Offloading (FPSO) unit, aiming to reduce greenhouse gas emissions and support the decarbonization of offshore oil and gas operations. FPSOs operate as weak-grid, islanded systems primarily powered by gas turbine generators (GTGs), where the introduction of inverter-based resources (IBRs) poses critical challenges to voltage and frequency stability. To address the high computational demands of modeling this complex hybrid microgrid—comprising GTGs, renewable hubs, and subsea feeders—a real-time simulation environment based on OPAL-RT is employed. This platform enables sub-millisecond integration steps, which would be infeasible using offline tools due to processing limitations. The system’s behavior is assessed under both steady-state and transient conditions, evaluating power quality and dynamic stability. Preliminary results show that increasing renewable energy penetration can significantly reduce reliance on GTGs while ensuring compliance with IEC 61892- 1:2019 standards, offering a scalable approach for advancing low-carbon offshore energy systems.

Keywords: Renewable Energy, Industry Applications, Power and Eneergy Systems
Abstract: The curtailment of renewable electricity in Brazil, especially in the Northeast, presents both a technical challenge and an opportunity for alternative energy applications. Despite the region's high renewable capacity, surplus energy remains underutilized in decarbonization strategies. A notable gap in the literature concerns the integration of geospatial and infrastructural variables to identify feasible regions for green hydrogen (GH₂) production. This study proposes a data-driven and geospatial methodology for estimating the theoretical GH₂ output from curtailed solar and wind power. Using national datasets from 2022 and 2024, the analysis identifies regions that exhibit spatial convergence in energy surplus, industrial hydrogen consumption, and water infrastructure. Results indicate a potential annual production exceeding 187.9 kilotonnes of GH₂ across four Northeastern states. Areas such as Camaçari (BA) and Pecém (CE) emerge as high-priority hubs. The findings contribute to the strategic planning of hydrogen infrastructure in Brazil, reinforcing the role of spatial analysis in energy transition frameworks.

Keywords: Artificial Intelligence, Blockchain Technology
Abstract: Digital transformation is a growing priority for healthcare institutions in Brazil, with more than 62% of organizations already considering digital in their strategic plans. However, only 18% have a structured digital strategy. This article explores the challenges and opportunities faced by Brazilian public hospitals on their journey towards digital maturity. Using a methodology based on the analysis of data from reports and case studies, the study highlights the importance of digital transformation for the efficiency and quality of healthcare services, focusing on innovations such as telemedicine and artificial intelligence. The article includes the discussion on barriers such as lack of governance, resistance to change, inadequate infrastructure, and lack of digital skills. Some examples of best practices and strategies to overcome these challenges include the creation of digital governance committees, investments in infrastructure and professional training, and the promotion of a culture of innovation. The study concludes that robust strategic planning and adequate investments can enable Brazilian hospitals to achieve effective digital transformation, resulting in significant improvements in the efficiency and quality of healthcare services. Implementation of clear governance, continuous training, and promoting a culture of innovation are essential to overcome barriers and maximize the benefits of digital technologies.

Keywords: Life Support Systems & Techniques, Big Data in Industry Applications, Cybernetic Systems
Abstract: The regulation of access to specialized care represents a persistent challenge within health systems, particularly in countries characterized by significant population and territorial heterogeneity, such as Brazil. This study aimed to analyze, through an integrative literature review, how different countries structure their regulatory systems with a focus on the integration of epidemiological, demographic, and mortality data. The methodology involved a systematic search in the PubMed and Scopus databases, resulting in the selection of 178 articles published between 2015 and 2025. The findings reveal a growing interest in digital and interoperable solutions, with particular emphasis on artificial intelligence, real-time surveillance, and data-driven triage systems. It was observed that governance models vary across countries; however, there is a consensus on the need for systemic integration to promote equity and efficiency. It is concluded that the improvement of systems such as SISREG requires the adoption of international best practices, focusing on interoperable infrastructure, intelligent prioritization, and collaborative governance, to achieve more effective regulation within the context of the Brazilian Unified Health System (SUS).

Keywords: Smart Grids, Power and Eneergy Systems
Abstract: This paper presents a methodology for improving protection coordination in industrial electrical systems by integrating logical and chronological selectivity through the IEC 61850 standard. The proposed approach combines GOOSE-based logical selectivity with backup chronological selectivity, supported by automatic switching between adjustment groups in case of communication failure. Experimental results demonstrated that the methodology ensures fast and selective operation under normal conditions and maintains coordination during adverse scenarios, avoiding misoperations. The practical application in a 13.8 kV substation of a mining plant validated the feasibility and robustness of the solution. This architecture provides a scalable and adaptive protection strategy aligned with the reliability and safety requirements of modern industrial systems.

Keywords: Life Support Systems & Techniques
Abstract: Last-mile logistics (LML) represents the final stage of the supply chain, characterized by urban challenges such as traffic congestion, high costs, and environmental impact. This research analyzed the use of bicycles as a sustainable alternative for deliveries in last-mile logistics in the city of São Paulo, highlighting environmental advances alongside social and labor-related challenges. The methodology employed was a Systematic Literature Review (SLR), selecting 18 relevant articles published since 2019. The results indicate that, despite environmental benefits and reduced logistical costs, delivery cyclists face precarious working conditions, long working hours, low wages, and a lack of adequate infrastructure. Movements such as "Breque dos Apps" have emerged to demand better working conditions. The research highlights the need for public policies to regulate the sector, expand cycling infrastructure, and recognize cycle logistics as a fundamental element for sustainable urban mobility. As a suggestion for future research, a comparative quantitative study between motorized deliveries and those carried out by cyclists is proposed.

Keywords: Renewable Energy, Smart Grids, Industry Applications
Abstract: Em virtude das crescentes preocupações ambientais, bem como o custo dos recursos energéticos, despertam o interesse para a utilização de biocombustíveis, em o biodiesel, bem como a mistura deste com combustíveis convencionais com o objetivo de reduzir custos, aumentar a eficiência de operação e reduzir a emissão de poluentes. Nesse contexto, o presente trabalho tem como objetivo determinar as principais propriedades físico químicas de misturas de óleo diesel e biodiesel, bem como avaliar o desempenho de um gerador de energia sem alteração mecânica, utilizando diferentes concentrações de misturas de diesel (S500) e biodiesel de soja e sebo bovino. Serão determinados o consumo de combustível, o consumo específico de combustível, a eficiência de energia e a intensidade sonora emitida pelo conjunto motor gerador da marca BRANCO para diferentes cargas resistivas. Para todas as misturas analisadas foi observado um aumento na densidade, viscosidade e tensão superficial com o aumento da porcentagem do biodiesel na mistura. Os menores valores de poder calorífico superior e inferior foram obtidos para a mistura com maior fração de biodiesel. Para todas as cargas resistivas o maior consumo horário e maior consumo específico foram obtidos para o diesel puro e o menor consumo horário e maior consumo específico foram obtidos para a mistura B30. Para todas as cargas resistivas a mistura B30 apresentou as maiores eficiências, indicando ser a mistura com melhores resultados em todos os testes do conjunto motor gerador.

Keywords: Machine Learning, Industry Data Science Applications, Industry Applications
Abstract: This work presents a comparative analysis of feature selection methods for fault classification in rotary machines using vibration data from Fiber Bragg Grating accelerometers. Features from the time, frequency, and time–frequency domains were selected using filter-based (Minimal Redundance Maximal Relevance, Laplacian Score), wrapper-based (Genetic Algorithm), and embedded (Random Forest, Kolmogorov-Arnold Network) approaches. The selected features were evaluated using supervised classifiers (Multilayer Perceptron, Quadratic Discriminant Analysis, Random Forest, and Kolmogorov-Arnold Network) and the unsupervised K-means algorithm. Genetic Algorithm and Random Forest methods achieved the best performance, reducing the number of features by a factor of eight while maintaining accuracies near 98% for supervised models. Feature selection also improved the K-means pipeline, leading to a significant increase in accuracy. Comparing the MLP and KAN models, KAN achieved performance comparable to that of the MLP while requiring fewer learnable parameters. Furthermore, KAN enables classification through explicit symbolic functions that can be readily deployed in embedded applications. This study contributes by systematically comparing feature selection techniques applied to data from Fiber Bragg Grating sensors in the context of motor fault diagnosis.

Keywords: Industry 4.0, Industry Applications
Abstract: Este artigo analisa em que medida o Programa de Mobilidade Verde e Inovação (MOVER) contribui para a capacitação da mão de obra e para a modernização tecnológica das ferramentarias brasileiras, em sintonia com a Indústria 4.0 (I4.0) e as perspectivas da Indústria 5.0 (I5.0). A pesquisa, de caráter quantitativo e exploratório, foi realizada com 34 empresas associadas à ABINFER, por meio de questionário estruturado em blocos temáticos. Os resultados indicaram ampla difusão de tecnologias consolidadas, como ERP/MES, CAD e CAM, mas baixa adoção de PDM, CAE, Big Data e gêmeos digitais. As demandas de capacitação abrangem engenheiros, operadores CNC, ferramenteiros e lideranças, reforçando a necessidade de formação contínua. As principais barreiras identificadas foram custos elevados, resistência cultural, falta de capacitação e limitações de infraestrutura. Conclui-se que o MOVER desempenha papel estratégico ao alinhar modernização tecnológica e qualificação profissional, ampliando a competitividade do setor.

Keywords: Industry Applications, Electrical Machines and Drives, Diagnosis, Prognosis and System Identification
Abstract: Flywheels are widely employed on motor test benches and Dynamic Load Emulation (DLE) platforms to replicate industrial drives' rotational inertia. Nevertheless, their dynamic behavior is seldom modeled or experimentally validated. This knowledge gap hampers the accuracy of transient-response studies and closed-loop control design. The present study rigorously assesses an inertial disk's conventional first-order Coulomb-viscous model and proposes a data-driven identification framework that dispenses with shaft-mounted torque sensors. Angular-speed rundown tests were performed on a 0.5 hp induction motor fitted with three disk assemblies spanning J = 0.704–0.759 kg·m². Twelve trials per assembly were sampled at 500 Hz. Particle Swarm Optimization (PSO) simultaneously estimated J, Coulomb friction FC, and viscous friction FV by minimizing the root-mean-square error (RMSE) between measured and simulated speed profiles. The first-order model reproduced the rundown trajectory with an RMSE of less than 1 rad/s for speeds below 200 r/min; for speeds greater than 600 r/min, the RMSE increased to more than 5 rad/s. The high-speed error is related to acceleration-dependent effects neglected in classical formulations, which may be associated with the jerk effect. These results demonstrate that (i) PSO can recover mechanical parameters directly from speed data, and (ii) the classical model cannot represent the dynamics of all operating points, mainly for higher inertial values. The proposed methodology strengthens the reliability of DLE setups and industrial motor diagnostics where accurate inertial emulation is critical.

Keywords: Cyber Physical Systems, Digital Twins and Knowledge Systems, Artificial Intelligence, Machine Learning
Abstract: A crescente digitalização dos serviços de saúde impõe desafios significativos à governança de sistemas públicos universais como o Sistema Único de Saúde (SUS) no Brasil. Este estudo analisa os impactos da implementação da transformação digital na governança do SUS, abrangendo as esferas federal, estadual e municipal. Utilizando uma abordagem de pesquisa descritiva e qualitativa, que incluiu revisão de literatura, análise documental, os impactos foram identificados e categorizados através da lente teórica da Ecologia Organizacional. Os resultados revelam um conjunto complexo de efeitos que afetam múltiplas funções da governança, incluindo desafios relacionados à tecnologia, cultura organizacional, capacitação profissional e colaboração interinstitucional. O trabalho conclui que a gestão eficaz da transformação digital no SUS requer uma abordagem sistêmica, focada no fortalecimento do ecossistema organizacional, para garantir a efetividade, a equidade e a sustentabilidade da governança em saúde na era digital. A análise fornece subsídios para a formulação de estratégias e ações mitigadoras por parte dos gestores do SUS.

Keywords: Industry Applications, Artificial Intelligence, Industry 4.0
Abstract: Este trabalho apresenta uma comparação detalhada entre métodos de Reconhecimento Óptico de Caracteres (OCR) aplicados à identificação de códigos alfanuméricos em placas de aço no ambiente industrial. São avaliados os modelos Tesseract, EasyOCR e PaddleOCR, incluindo uma versão ajustada (ajuste fino) do PaddleOCR. Os resultados mostram que o PaddleOCR ajustado alcança 98,08% de acurácia por caractere e 97,12% na identificação completa, superando as demais abordagens. Técnicas de pré-processamento digital de imagens são exploradas para otimizar a entrada do OCR. Além disso, foi implementado um sistema automatizado em tempo real para validação prática.

Keywords: Smart Grids, Machine Learning, Power and Eneergy Systems
Abstract: The transition toward a modernized power grid founded on smart grid technologies requires the accurate identification of potential subsystems through the analysis of distributed generation, energy storage, and load patterns among clustered prosumers in specific regions. This task becomes particularly complex in large-scale, geographically dispersed distribution systems, especially in agricultural areas characterized by seasonally intensive energy demands. For the bottom-up development of smart grid applications in farmlands, it is essential to identify multi-microgrid systems as clusters of interconnected microgrids, considering parameters such as feeder line impedance, seasonal variations in power demand, distributed generation availability, and energy storage capacity. Identifying optimal combinations of prosumers facilitates the formation of multi-microgrid structures, enabling enhanced techno-economic and environmental performance while promoting collaborative solutions to grid-related challenges. This study investigates machine-learning-assisted techniques for system identification to address this problem. The results demonstrate the effectiveness of the proposed approach in identifying multi-microgrid systems using a generic CIGRE benchmark distribution network, thereby contributing to the development of data-driven models for complex energy systems in the agri-food sector.

Keywords: Renewable Energy, Power and Eneergy Systems, Industry Data Science Applications
Abstract: This study aims to enhance the forecasting accuracy of photovoltaic (PV) power generation by developing a hybrid model that integrates statistical performance analyses tailored to specific meteorological conditions. The primary objective is to construct a Combined Statistical Model by Performance (CSMP) that optimizes the predictive capabilities of individual PV generation models based on their performance across distinct irradiance and temperature ranges. Specific objectives include analyzing daily peak irradiance as the primary model selection criterion, evaluating model performance within defined irradiance intervals, incorporating air temperature to improve predictions under high irradiance conditions, and integrating these analyses using statistical metrics. The study utilizes two years of solar photovoltaic generation data from 45 kW PV systems installed at the Polytechnic Institute of Bragança (IPB), Portugal. Four statistical metrics—root mean square error (RMSE), mean bias error (MBE), mean absolute error (MAE), and percentage variation—were employed to assess model performance across various time horizons, from one day to one year. The CSMP demonstrated superior performance compared to individual models, achieving an average improvement of 7.3% in energy prediction accuracy, highlighting the effectiveness of irradiance- and temperature-based model optimization for reliable PV forecasting.

Keywords: Industry Applications, Industry 4.0, Artificial Intelligence
Abstract: Digital health is a key pillar in transforming health system, fostering innovation, improving efficiency, and expanded access. To promote digital integration and strengthen the Unified Health System (SUS), the Digital Health Strategy 2020–2028 (ESD 2028) was formulated and implemented. Nevertheless, Brazil still faces significant challenges in five core areas: infrastructure, interoperability, digital literacy, inequality, and governance. This article presents a state-of-the-art review based on scientific and gray literature (2020–2025), employing thematic coding and comparative analysis. The Brazilian experience is critically contrasted with the digital health policies of Argentina, Chile, Uruguay, Peru, and Colombia. Although results indicate that Brazil has advanced in expanding connectivity and in implementing national initiatives such as the National Health Data Network (RNDS), difficulties remain in ensuring governance continuity and equitable access. Lessons learned from Uruguay’s unified electronic health record and Chile’s digital literacy programs highlight potential paths for adaptation. Beyond describing barriers, this review emphasizes the need for institutional stability, investment in human capacity, and regional collaboration. Mapping these challenges and lessons contributes to the debate on achieving sustainable digital health in Latin America.

Keywords: Optimization Heuristics and Methods, Power and Eneergy Systems, Renewable Energy
Abstract: This review analyzes metaheuristic approaches for optimal Energy Storage System (ESS) allocation in distribution networks by addressing four key research questions: application scenarios, metaheuristic techniques, objective functions, and constraints. The study identifies five primary application categories-cost reduction, renewable intermittency mitigation, voltage regulation, power loss minimization, and emerging uses such as mobile ESS-while highlighting the prevalence of hybrid algorithms (e.g., PSO-GWO) and bio-inspired methods. The results indicate a shift from purely economic objectives to multiobjective frameworks that incorporate technical, environmental, and resilience metrics, although standardization gaps remain in areas such as uncertainty modeling and policy integration. The review underscores advancements in machine learning enhanced metaheuristics and emphasizes the need for broader regional analyses and real-world validations to support sustainable energy transitions.

Keywords: Renewable Energy, Power and Eneergy Systems
Abstract: Este trabalho oferece a primeira síntese abrangente sobre sistemas híbridos solar hidrelétricos no Brasil, combinando análise técnico operacional e revisão crítica do arcabouço regulatório até o primeiro trimestre de 2025. A partir da consolidação de estatísticas dispersas, são mapeados dez empreendimentos de grande porte no exterior e todos os projetos piloto nacionais, evidenciando ganhos como elevação do fator de capacidade, redução de perdas por evaporação e mitigação de emissões. O estudo demonstra que, embora a Resolução ANEEL 954/2021 e a REN 1 068/2023 já reconheçam a “usina híbrida”, persistem lacunas: licenciamento ambiental fragmentado entre ANEEL, ANA e Ibama, ausência de regras específicas para contabilização no MRE e rateio da TUST, além de curtailment solar recorrente por restrições de transmissão. Como contribuição principal, propõe-se um marco integrado de outorga e licenciamento com canal digital único, leilões de complementaridade que remunerem serviços sistêmicos e linhas de financiamento de longo prazo, criando segurança jurídica e condições de mercado para viabilizar o portfólio de projetos híbridos em análise. Essa agenda regulatória, aliada à queda contínua do LCOE fotovoltaico e ao avanço de tecnologias de armazenamento, coloca o Brasil em posição estratégica para liderar a expansão global de sistemas híbridos e acelerar a descarbonização de sua matriz elétrica.

Keywords: Electrical Machines and Drives, Industry Applications, Power Electronics
Abstract: Model predictive current control (MPCC) is a robust strategy for controlling electrical machines such as induction motors (IM), through an optimal selection of voltage vectors using a dedicated control algorithm. However, the successful application of MPCC depends on the availability of highly accurate motor parameters, which may not be accessible in certain scenarios. When these stringent requirements are unmet, both dynamic and steady-state behaviors can be affected. To enhance control robustness in the presence of parameter mismatches, this study proposes a hybrid approach that integrates the Two-Vector Predictive Current Control (MPCC2V) with Model-Free Predictive Current Control (MPCCMF) that incorporates a Linear Extended State Observer (LESO) to mitigate disturbances and improve overall performance effectively. This method leverages accessible system input/output data in combination with estimated parameter values, resulting in enhanced robustness and improved steady-state performance by reducing ripples and deviations. The results confirm that the Two-Vector Model-Free Predictive Current Control (MPCC2VMF) strategy can be successfully implemented, effectively integrating the advantages of both foundational approaches upon which it is based.

Keywords: Industry Applications, Power Quality, Electrical Machines and Drives
Abstract: Rising energy costs, coupled with environmental concerns, have led industries to seek new ways of managing power consumption. In this context, demand side management emerges as essential tools for optimizing electric power, reducing operating costs and minimizing environmental impacts. Within the industrial environment, the infrastructure installed for Distribution Network Operators is based on a specific demand. The power consumption established between the final consumer and the Distribution Network Operators is an important topic to be respected, especially in the industrial sector, where factories have been constantly growing due to the need to increase production. This paper proposes the automation of grain dryers for efficient side management in an industrial environment. A motor control logic was proposed for this system to protect the transformer, optimize the drying logic and control the energy demand obtained from distribution. For this purpose, controllers, meters and a SCADA system were used, communicating via the Profinet and Modbus protocols. The results showed that the application of the automation system can generate considerable cost savings for the company

Keywords: Power Electronics, Automation and Process Control, Power and Eneergy Systems
Abstract: This paper presents a comprehensive energy-based modelling and optimal control framework for Buck converters, integrating non-ideal components such as switch resistance, diode reverse recovery voltage and resistance, inductor DCR, and capacitor ESR. By leveraging a Lagrangian formulation, the proposed approach captures the physical dynamics of the converter while preserving analytical tractability. The resulting nonlinear model is systematically linearized to enable the design of an optimal state-space controller. To enhance performance and reduce tuning effort, Particle Swarm Optimization (PSO) is employed to automatically determine the weighting matrices of the Linear Quadratic Regulator (LQR). The PSO-tuned controller significantly improves dynamic response and robustness under varying operating conditions. Numerical simulations confirm the accuracy of the energy-based model and the effectiveness of the optimized control strategy in minimizing output voltage deviation during transients, outperforming conventional averaged models. This work highlights the potential of combining physically grounded modelling with metaheuristic optimization for advanced power converter control.

Keywords: Electrical Machines and Drives
Abstract: Although the Linear Induction Motor was initially conceived and developed as a power machine for high-speed rail transportation, further research has opened up many new avenues for its application, where translational motion is finally required. Notable in this context is the Electromagnetic Launch System (EMLS) technology for quickly launching airborne objects like aircrafts and UAVs from short platforms like ship decks. A highly suitable configuration for such an application is the Long-Primary Double-Sided Linear Induction Motor (LPDSLIM) with a short mover carrying the required payload. This paper covers the preliminary design of the stator for rapidly launching a projectile of mass 250 kg at 40 m/s and its optimization, followed by simulation and evaluation of performance of the system using Finite Element Method (FEM). The approach spans the concepts of the Equivalent Circuit Model (ECM), machine parameter computations and Particle Swarm Optimization (PSO) in the formulation, while ANSYS-MAXWEL 2D software is employed for simulation and determination of electromagnetic and electro-mechanical characteristics of the drive system for validation of the optimized design.

Keywords: Deep Learning and Machine Learning, Industry 4.0, Diagnosis, Prognosis and System Identification
Abstract: Este estudo apresenta uma análise comparativa entre regressão linear múltipla e redes neurais artificiais para prever a temperatura ideal de secagem em um secador de fibras de madeira, com o objetivo de reduzir a variabilidade de umidade na saída do processo. Na fabricação de painéis MDF (Medium Density Fiberboard), o controle preciso da umidade das fibras é essencial para garantir a qualidade e as propriedades tecnológicas do produto final, tornando a umidade uma variável crítica no processo de produção. A primeira abordagem emprega um modelo de regressão linear múltipla, desenvolvido com base em parâmetros-chave do processo selecionados com a ajuda de especialistas, para estimar a temperatura ideal de secagem. A segunda abordagem utiliza uma rede neural do tipo Multi-Layer Perceptron (MLP), treinada com as mesmas variáveis, para aprimorar a precisão das previsões. O estudo inclui a formulação do modelo de regressão e a avaliação de diferentes topologias de MLP. Ambas as metodologias capturaram com sucesso a dinâmica do processo industrial; no entanto, o modelo de MLP demonstrou desempenho superior. Os modelos desenvolvidos foram posteriormente testados em um ambiente de simulação, o que possibilitou a comparação com os pontos de ajuste de temperatura definidos manualmente pelos operadores. Os resultados confirmaram que a abordagem baseada em rede neural obteve maior precisão e maior adaptabilidade às variações do processo.

Keywords: Industry 4.0, Cyber Physical Systems, Digital Twins and Knowledge Systems, Virtualization, Simulation Techniques and Augmented Reality
Abstract: A evolução da Indústria 4.0 tem impulsionado a modernização de processos industriais, promovendo maior digitalização, conectividade e automação em diferentes setores. Entretanto, a integração de sistemas industriais com tecnologias digitais ainda representa um desafio. Este trabalho apresenta o desenvolvimento de um Asset Administration Shell (AAS) reativo aplicado a um robô SCARA simulado, com o objetivo de representar digitalmente suas variáveis operacionais em conformidade com o modelo de referência RAMI 4.0. A arquitetura foi estruturada com base em um cliente OPC UA assíncrono para aquisição de dados, um middleware desenvolvido em Python com FastAPI, persistência via banco de dados SQLite e visualização interativa por meio de dashboard em Streamlit. O submodelo OperationalData foi modelado utilizando o AASX Package Explorer e atualizado ciclicamente com dados das juntas, tempo de ciclo e estado do robô. A proposta foi validada em ambiente local, com potencial de migração para plataformas embarcadas e aplicações edge. Os resultados confirmam a viabilidade da solução para digitalização de ativos industriais, permitindo monitoramento em tempo real e promovendo a interoperabilidade entre sistemas físicos e digitais, em alinhamento com os princípios da Indústria 4.0.

Keywords: Power Quality, Power and Eneergy Systems, Renewable Energy
Abstract: Energy security stands as a fundamental pillar for the operational stability and sustainable development of electrical power systems, especially in the context of rising global electricity demand and the increasing integration of intermittent renewable energy sources into the energy matrix. This study proposes a quantitative approach to assess the contribution of generation source diversification to energy security, through the formulation of a composite index termed the Energy Security Factor (ESF). The methodology integrates classical probabilistic metrics—such as the Herfindahl-Hirschman Index (HHI), Loss of Load Probability (LOLP), and Expected Energy Not Supplied (EENS)—weighted using Shannon Entropy in order to capture both the variability and the relative significance of each indicator, among others. The IEEE 14-bus system is employed as the case study, enabling a comparative analysis of scenarios composed of dispatchable (hydroelectric and thermal) and non-dispatchable (solar and wind) sources, as well as energy storage systems. This approach aims to bridge the structural diversity of the energy mix with system reliability metrics, allowing for a holistic assessment of energy security. The expected outcomes have the potential to inform public policy and support the development of more robust energy planning strategies, contributing to the design of resilient and adaptive power systems aligned with the challenges of the global energy transition.

Keywords: Life Support Systems & Techniques, Industry 4.0, Automation and Process Control
Abstract: Introduction: Process mapping (PM) is used in the healthcare sector to visualize workflows, identify inefficiencies, bottlenecks and opportunities for improvement. This research aims to analyze the profile of scientific production on process mapping in the healthcare sector. Methods: A bibliometric review was conducted using data collected in May 2025 from Scopus and Web of Science. Data was processed with the Bibliometrix package in RStudio, while Microsoft Excel was employed to enhance visualization when necessary. Results and Discussion: From 2015 to 2025, 5,707 unique articles were retrieved, of which 2,793 addressed different applications of PM, reflecting the thematic diversity of healthcare. Scientific output is concentrated in the United States (31%), with the University of Toronto as the most productive institution, whereas the most impactful authors are affiliated with Chinese institutions. Trend topic analysis revealed strong associations between terms such as health, processes, and process management, underscoring the connection between digital process analysis and healthcare delivery. Thematic mapping identified a motor cluster—including process modeling, process design, and procedures—indicating the influence of emerging technologies that require updated procedures and process reconfiguration. Conclusion: The bibliometric analysis highlights gaps such as the absence of standardized guidelines and the need for clearer standards for PM application. International collaboration remains limited and dispersed, presenting opportunities for stronger global scientific interaction in the future.

Keywords: Renewable Energy, Power Quality, Power and Eneergy Systems
Abstract: The article explores the use of open and publicly available data from the Distributor’s Geographic Database (BDGD) to model and simulate active electrical distribution networks, focusing on energy sustainability. Open-source tools such as QGIS and OpenDSS were applied to assess real networks and scenarios for implementing distributed generation (DG) in public buildings within smart cities. Technical analyses of power flow, voltage profile, technical losses, and hosting capacity enabled an integrated evaluation of network performance and economic impacts. Results highlight that DG location and self-consumption profiles are crucial to minimizing losses, avoiding overloads, and preventing voltage violations. The proposed replicable approach, combining open data, simulation, and technical analysis, supports strategic decision-making and the development of resilient, intelligent networks aligned with the sustainable energy transition.

Keywords: Optimization Heuristics and Methods, Power Electronics, Power Quality
Abstract: The increasing presence of non-linear loads in electrical power systems (EPS) has driven extensive research into mitigating the associated problems, particularly the presence of harmonic content. This phenomenon can cause undesirable effects in EPS and shorten the useful life of electrical equipment. One effective solution to reduce harmonic distortion is the strategic allocation of Active Power Filters (APFs), which use DC-AC converters to produce alternating voltage or current with desirable waveforms. Optimally placing these devices, in order to achieve better technical and economic performance, represents a complex optimization challenge, as it involves solving a nonlinear, multi-modal problem. Various techniques can be employed to address this issue, including metaheuristic approaches. Among them, Particle Swarm Optimization (PSO) stands out due to its effectiveness and ease of implementation. This paper presents a methodology for the optimal allocation of APFs in electrical distribution systems using PSO, with the objective of minimizing the total harmonic distortion (THD) of current signals. The proposed methodology has been implemented computationally, and OpenDSS has been used to perform the harmonic analysis. The IEEE 13-Bus Feeder system serves as the test case for evaluating the proposed method.

Keywords: Cyber Physical Systems, Digital Twins and Knowledge Systems, Electrical Vehicle and Energy Storage, Renewable Energy
Abstract: The electrification of transportation is a key strategy for reducing emissions, particularly in the Amazon region, where river transport is predominant and still relies on internal combustion engines. This work investigates the electromagnetic performance of three axial flux permanent magnet motor topologies: single rotor with single stator, single rotor with double stator, and double rotor with single stator, aiming at their application in small riverine vessels. Digital Prototypes were developed as computational representations of designs under development, allowing functional analysis through simulations in Motor-CAD using the Finite Element Method. The study evaluated magnetic flux density distribution, electromagnetic torque, and total losses. Results show that the double stator and double rotor configurations perform better overall, with the double rotor single stator emerging as the most suitable option for electric propulsion in Amazonian inland navigation. This study is part of the project “Small-Scale Electric River Vessel with a Charging Station Network to Serve Riverside Communities in the Amazon” (PD-07427-0223/2023), funded by Norte Energia S.A. through the R&D Program of ANEEL.

Keywords: Industry 4.0, Deep Learning and Machine Learning, Big Data in Industry Applications
Abstract: The qualification of professionals within Brazil’s Unified Health System (SUS), amidst the ongoing digital transformation, has become a strategic axis for the effectiveness of public policies. This article presents the design and initial implementation of a strategy developed by the Laboratory of Design Thinking, Management and Engineering (LabDGE/UFF), originally validated within the Ministry of Agriculture and Livestock (MAPA) and now adapted to the SUS. The model integrates micro-credentials and digital learning systems, structuring pathways focused on digital foundations, automation, innovation, and leadership. It features short-term modules, formal recognition of competencies, and continuous evaluation. The virtual learning environment enables personalization, secure certification, and performance monitoring. Data from the pilot phase indicate both feasibility and challenges, particularly regarding the integration of certifications into personnel management and reaching vulnerable regions. The experience is analyzed within the broader context of innovation in public sector learning, highlighting its replicability potential and institutional limitations.

Keywords: Artificial Intelligence, Human Machine Symbiosis, Industry 4.0
Abstract: A Inteligência Artificial (IA) está transformando rapidamente os contextos industriais e sociais, proporcionando ganhos inéditos de eficiência. No entanto, essa transformação traz preocupações éticas urgentes, especialmente no que se refere à substituição de trabalhadores e à obsolescência de competências, agravadas pela ausência de mecanismos adequados de apoio e requalificação. Este artigo tem como objetivo analisar criticamente as implicações éticas da automação do trabalho baseada em IA, com ênfase na adoção de uma IA Centrada no Humano (HCAI) como paradigma mais responsável e sustentável. As principais contribuições incluem: (i) uma revisão sistemática dos conceitos de Indústria 4.0, Indústria 5.0 e HCAI sob uma perspectiva ética; (ii) a articulação dos desafios da automação à luz dos princípios de justiça social; e (iii) a proposição de diretrizes para políticas públicas, práticas educacionais e responsabilidade corporativa voltadas à promoção de uma transição tecnológica justa e inclusiva. Como resultado, o estudo identifica lacunas críticas nas políticas atuais de automação e destaca a urgência ética de uma mudança de modelos orientados apenas pela eficiência para inovações centradas no ser humano. O artigo conclui com um conjunto de recomendações normativas e práticas que reforçam a importância da equidade, sustentabilidade e resiliência na configuração do futuro do trabalho.

Keywords: Power and Eneergy Systems, Optimization Heuristics and Methods
Abstract: A proposta deste estudo é aplicar o algoritmo de sensibilidade de trajetória na estimação dos parâmetros de sequência positiva e sequência zero de linhas de transmissão. A precisão desses parâmetros é essencial para a operação segura e eficiente dos sistemas elétricos de potência (SEP), sendo amplamente utilizada na parametrização e configuração de dispositivos de proteção. Tradicionalmente, métodos consolidados, como os modelos de Bessel e Carson, são empregados na representação das linhas de transmissão. No entanto, devido às aproximações envolvidas e à influência de diversos fatores nos cálculos, novas abordagens vêm sendo exploradas. Nesse contexto, o algoritmo de sensibilidade de trajetória se apresenta como uma alternativa robusta, formulada neste trabalho como um problema de otimização não linear. Para avaliar sua eficácia, foram realizados testes em diferentes cenários operacionais. Os resultados indicam que o método proporciona alta precisão na estimativa dos parâmetros de sequência positiva, mesmo diante de variações no comprimento e no carregamento da linha. Por outro lado, a estimação dos parâmetros de sequência zero não apresentou desempenho satisfatório, exigindo ensaios específicos em que a sensibilidade do algoritmo seja mais pronunciada. Dessa forma, conclui-se que o algoritmo de sensibilidade de trajetória é uma abordagem promissora e viável para aplicações em estudos de sistemas de potência, especialmente na estimação de parâmetros de sequência positiva.

Keywords: Renewable Energy, Power Quality, Power and Eneergy Systems
Abstract: This paper introduces a stochastic methodology for photovoltaic systems (PV) and battery energy storage (BESS) allocation in medium-voltage distribution networks with mixed residential, commercial, and industrial loads. The proposed approach simulates multiple PV penetration scenarios using Monte Carlo method, accounting for monthly irradiance and temperature profiles to reflect the seasonal variability. Battery units are proportionally allocated and strategically dispatched, charging during PV generation hours and discharging during evening peaks. Simulation results demonstrate that PV and BESS integration significantly improves voltage profiles, especially at penetration levels between 130% and 160%. However, excessive PV integration beyond 250% increases overvoltage risk due to reverse power flow. The analysis highlights the importance of coordinated PV and BESS and identifies an optimal range of penetration to maximize hosting capacity while maintaining network stability

Keywords: Robotics and Mechatronics, Internet of Things
Abstract: This article explores the intersection between computational thinking, educational robotics, ethnoinformatics, and the guidelines of the National Common Curricular Base (BNCC), focusing on the training of indigenous educators. The research highlights the importance of computational thinking as an essential competence in the 21st century and educational robotics as an effective tool for its implementation. In addition, it discusses ethnoinformatics as an approach that integrates technology and cultural practices, promoting meaningful and contextualized education. Through the analysis of practical activities with Braço do Saber (name in Portuguese that means Arm of Knowledge), the article highlights how these approaches can contribute to a more inclusive education and prepare students for contemporary challenges.

Keywords: Electrical Vehicle and Energy Storage, Optimization Heuristics and Methods, Renewable Energy
Abstract: This paper proposes an optimization strategy for the operation of isolated microgrids with the integration of electric vehicles (EVs). The developed model incorporates multiple generation sources, including solar, wind, and a diesel generator as backup, while considering distinct EV user profiles, each with specific connection patterns to the grid and mobility requirements. The main objective is to minimize fossil fuel consumption through a Mixed-Integer Linear Programming (MILP) model, ensuring the efficient operation of the microgrid and proper allocation of EV charging and discharging. The results demonstrate that the optimized coordination of EV charging and discharging periods significantly reduces the need for diesel generator activation while maximizing the use of available renewable sources. The proposed methodology represents an advancement in energy management strategies for isolated microgrids, contributing to a sustainable energy transition and mitigating environmental impacts from fossil fuel use.

Keywords: Renewable Energy, Diagnosis, Prognosis and System Identification, Power and Eneergy Systems
Abstract: The increasing expansion of photovoltaic solar energy has heightened the demand for accessible and effective methods for inspecting and monitoring the integrity of photovoltaic modules. In this context, the electroluminescence technique emerges as a promising tool for detecting defects such as microcracks, electrical disconnections, and cell degradation. This study investigated the capability of a conventional digital camera, adapted to capture electroluminescence images of educational photovoltaic modules. Tests were conducted with variations in short-circuit current, alongside the application of image processing techniques to enhance the visualization of electroluminescent emissions. The results demonstrated that the adapted camera was capable of recording infrared emissions, although the signal intensity varied depending on the module. Furthermore, the image processing stage proved essential for enhancing emission patterns and facilitating defect identification. The proposed methodology exhibited significant potential for application in academic settings and preventive maintenance of photovoltaic systems, enabling an accessible and efficient diagnosis of module integrity.

Keywords: Optimization Heuristics and Methods, Artificial Intelligence, Industry 4.0
Abstract: Abstract — With the increasing demand for improved productivity, operational efficiency, safety, profitability, and cost reduction in industrial processes, optimization has become a critical focus. In the steel manufacturing sector, the optimization of box annealing furnace operations represents a strategic initiative to enhance industrial competitiveness. This problem is modeled as a multi-objective, NP-hard optimization problem. This study presents a methodology based on selective evolutionary strategies to determine the optimal allocation of coil loads within box annealing furnaces. A computational tool was developed using MATLAB®, featuring a graphical user interface that supports supervisory control, real-time execution, monitoring, and result visualization. The proposed approach leverages genetic algorithms, with input parameters derived from the technical specifications of real coil inventory. The optimization model integrates production cycle constraints, furnace capacity limitations, and industrial loading regulations, ensuring compliance with operational and safety standards. Simulation and experimental results demonstrate that the use of genetic algorithms provides an effective, robust, and practical solution for optimizing the annealing process. The proposed methodology is validated through experiments conducted in a steel plant and contributes to improved load balancing, enhanced energy efficiency, and superior final product quality.

Keywords— genetic algorithms, coil loading optimization, box annealing furnace, process control, heuristic optimization techniques

Keywords: Electrical Machines and Drives, Electrical Vehicle and Energy Storage, Virtualization, Simulation Techniques and Augmented Reality
Abstract: Este trabalho apresenta o projeto, modelagem computacional e análise comparativa de um motor Brushless DC (BLDC) de 5 HP (3,73 kW), desenvolvido especificamente para aplicações em veículos elétricos de médio porte. Entre as aplicações estudadas incluem-se bicicletas elétricas, motocicletas, pequenas embarcações e veículos urbanos leves. Os motores BLDC demonstraram possuir vantagens significativas quando comparados aos motores tradicionais, destacando-se pela maior eficiência energética (com valores típicos entre 85-97%, contra 70-85% dos motores a combustão), menor necessidade de manutenção (devido à ausência de escovas), melhor relação torque/peso e controle de velocidade mais preciso.

Para a realização deste estudo, foi empregado o método dos elementos finitos (MEF) através do software Ansys Motor-CAD, permitindo uma avaliação detalhada de dois materiais magnéticos para o núcleo do motor: o JFE 10JNEX900, um aço elétrico de grão orientado de alta performance, e o M250-35A, um aço silício convencional.

Os resultados obtidos revelaram que o material JFE 10JNEX900 apresentou menores perdas no núcleo (180,05 W em comparação com 184,8 W do M250-35A sob carga) e maior estabilidade térmica. Por outro lado, o M250-35A mostrou um torque médio 2,8% superior (12,647 Nm contra 12,349 Nm). A análise do mapa de eficiência identificou uma faixa ótima de operação em torno de 3000 rpm e 20 Nm, onde o motor alcança eficiência de até 97%.

Além de confirmar as vantagens intrínsecas dos motores BLDC, o estudo demonstrou que a seleção criteriosa de materiais e configurações construtivas pode melhorar em até 22% o desempenho energético em aplicações veiculares. Esses resultados oferecem critérios técnicos valiosos para o desenvolvimento de sistemas de propulsão elétrica mais eficientes,

Keywords: Resource Efficienty & Circular Economy Tracking, Industry 4.0, Industry Applications
Abstract: Desde 2018, a cidade de Maceió enfrenta um desastre geotécnico causado pela extração de sal-gema, resultando no afundamento do solo e na evacuação de mais de 14 mil imóveis. Este artigo propõe transformar esse passivo urbano em uma oportunidade por meio da reciclagem dos resíduos da construção e demolição (RCD) gerados. Utilizando uma abordagem quantitativa, o estudo adapta o modelo de Calderoni (2003) para estimar os ganhos econômicos potenciais com base no volume de RCD produzido, nos custos evitados, na geração de empregos e na arrecadação fiscal. Foram utilizados dados secundários de órgãos oficiais e literatura técnica. Os resultados preliminares indicam que mais de 218 mil toneladas de resíduos poderiam ser reaproveitadas, gerando benefícios econômicos para diferentes agentes — incluindo o município, governos estadual e federal, setor industrial e sociedade. A pesquisa reforça a importância de políticas públicas integradas para viabilizar a economia circular em contextos de crise urbana

Keywords: Diagnosis, Prognosis and System Identification, Electrical Machines and Drives, Machine Learning
Abstract: Falhas em rolamentos estão entre as mais frequentes em motores de indução trifásicos usados em ambientes industriais. Devido à ampla aplicação desses motores na indústria, suas falhas podem resultar em prejuízos significativos, especialmente em casos de paradas não programadas nas linhas de produção. Atualmente, diversos métodos usam sinais coletados de forma invasiva e/ou são baseados em sistemas inteligentes de alta complexidade para a detecção das falhas. Assim, neste estudo, apresenta-se uma metodologia com sinais de emissão acústica que podem ser coletados de forma não invasiva e com sensores de baixo custo. Além disso, esses sinais apresentam assinaturas das falhas similares às dos sinais de vibração, geralmente usados para essa aplicação. Analisam-se os valores do espectro de frequência em 30, 60 e 90 Hz, os quais estão relacionados às falhas de rolamento em sinais de vibração. Avaliam-se classificadores com baixa complexidade, como máquinas de vetores de suporte, de forma a se obter uma solução passível de ser construída em um sistema embarcado. Os resultados de acurácia foram próximos a 98% mesmo quando considerados sinais coletados com motores sofrendo desequilíbrio elétrico da fonte de alimentação da rede.

Keywords: Cyber Physical Systems, Digital Twins and Knowledge Systems, Virtualization, Simulation Techniques and Augmented Reality, Industry Applications
Abstract: Retailing is a field that can take great advantage of simulation models such as Digital Shadows and Digital Twins to support many kinds of logistical operations inside its establishments. Despite its relevance, inventory management has been one of the essential retail operations that benefits least from these dynamic simulation models to support decision making. By integrating computer vision techniques, relational databases, and simulation tools, this work presents the proposal of a Digital Shadow capable of creating a reliable unidirectional connection of a shelf containing products and its virtual counterpart. The main findings illustrate a practical solution that can act as a significant step towards achieving a Digital Twin solution regarding inventory management and a promising foundation for those interested in integrating popular technologies to develop innovative Digital Shadows solutions.

Keywords: Artificial Intelligence, Internet of Things, Autonomous Vehicles and Drones
Abstract: The increasing demand for artificial intelligence (AI) inference at the edge introduces a significant challenge: achieving true portability across heterogeneous hardware architectures with minimal overhead. This paper presents a modular and low-footprint solution based entirely on WebAssembly (WASM), enabling convolutional neural networks (CNNs) to execute directly on microcontrollers such as the ESP32-CAM. All neural logic is manually implemented in WebAssembly Text Format (WAT), compiled to texttt{.wasm}, and executed using the WebAssembly Micro Runtime (WAMR). The proposed firmware supports image acquisition, RGB565 decoding, and preliminary convolution routines. Experimental results confirm that the same binary runs unmodified on x86, ARM64 (Raspberry Pi), and Xtensa (ESP32) architectures without recompilation. These findings validate WebAssembly as a viable abstraction layer for AI portability and transparency on resource-constrained edge devices.

Keywords: Diagnosis, Prognosis and System Identification, Life Support Systems & Techniques, Industry 4.0
Abstract: Introduction: This study aims to identify the main barriers and strategies to strengthen systemic interoperability in public health services and to support the development of sustainable policies. Methods: A systematic literature review was conducted based on PRISMA 2020 guidelines. Searches were performed in Scopus, Web of Science, MEDLINE/PubMed, and the Brazilian Digital Library of Theses and Dissertations (BDTD), focusing on open-access studies published between 2014 and 2025. Eligibility criteria included full-text studies addressing interoperability, public health, and process management. Data were analyzed qualitatively and thematically categorized. Results: Of the 61 records, 18 met the inclusion criteria. The challenges identified were grouped into technical, organizational, governance, and data related. The proposed strategies included the adoption of international standards, semantic web technologies, federated platforms, investment in training and infrastructure, process management approaches, and patient-centered care models. Overcoming interoperability barriers requires a combination of technological solutions, robust governance, strategic planning, and organizational process redesign. The Brazilian context still faces structural and political challenges that hinder large-scale implementation. Conclusion: Advancing interoperability demands a sociotechnical approach aligned with long-term public policy. It involves investing in infrastructure, digital maturity, regulatory frameworks, and organizational change, recognizing interoperability as a strategic pillar for achieving a resilient and equitable health system.

Keywords: Diagnosis, Prognosis and System Identification, Power and Eneergy Systems, Industry Applications
Abstract: This paper presents an experimental study on the application of noise reduction techniques to real partial discharge (PD) signals, affected by quantization noise and electromagnetic interference. The effectiveness of combining Variational Mode Decomposition (VMD), Least Mean Squares (LMS) adaptive filtering, and Wavelet Transform (WT) with hard and soft thresholding is evaluated. These techniques are applied to PD signals detected using a High-Frequency Current Transformer (HFCT) coupled to a 145 kV Inductive Voltage Transformer (IVT) and acquired with an 8-bit digital oscilloscope, with 1 GS/s sampling rate and 10 Mpts memory depth. The results demonstrate that the combination of these techniques provides significant improvements in terms of Noise Reduction Ratio (NRR) and contributes to preserving essential waveform characteristics for further analysis.

Keywords: Internet of Things, Virtualization, Simulation Techniques and Augmented Reality, Smart Grids
Abstract: Este artigo apresenta a implantação e a análise de desempenho de um sistema de energia renovável baseado em middleware em um ambiente virtual no campus da Universidade Federal do Pará. Sua análise de desempenho envolveu a operação ociosa de três máquinas virtuais (VMs) executando os middlewares de IoT Dojot, FIWARE e NodeRED, com um sistema de observabilidade sendo implantado em uma quarta VM integrando Proxmox VE, InfluxDB e Grafana. As principais métricas incluem uso de CPU, I/O wait, Loadavg, uso de RAM, L/E de disco e E/S de tráfego de rede. Os resultados mostram um sistema bem dimensionado e variações de desempenho estáveis para cada VM analisada e um sistema descomplicado e eficiente para monitorar os recursos do servidor por meio da integração do Proxmox VE, InfluxDB e Grafana. Trabalhos futuros se concentrarão em expandir a análise de recursos para um estado de produção, com cenários simulados, bem como o sistema de observabilidade para coletar e analisar logs e traces, dando suporte à implantação de alertas e gatilhos baseados no monitoramento implantado.

Keywords: Industry Data Science Applications, Big Data in Industry Applications, Cyber Physical Systems, Digital Twins and Knowledge Systems
Abstract: DATASUS provides one of the largest public health data repositories in the world, integrating health information systems across Brazil. Despite its richness, data fragmentation, lack of standardization, and delays in updates pose significant challenges. This paper discusses the construction of health management dashboards based on DATASUS, leveraging OLAP architectures and tools like Power BI to overcome these challenges.

Keywords: Industry 4.0, Optimization Heuristics and Methods, Life Support Systems & Techniques
Abstract: Diabetes is a growing global health concern, affecting approximately 422 million people and causing 1.5 million deaths annually, according to the World Health Organization (WHO). In Brazil, 15.7 million people were living with diabetes in 2021, an increase of 3.3 million over the past decade. This article presents an innovative approach using the hybrid method CRITIC-GRA-3N to select glucose meters, aiming to improve glycemic control and support diabetes management. The study evaluates criteria such as accuracy, connectivity, cost, and sustainability practices, offering a robust and replicable solution for the healthcare industry. The method reduces subjectivity and ensures consistency in ranking alternatives, contributing to the integration of academia and industry by leveraging advanced computational tools. The results highlight the importance of criteria such as energy efficiency and sustainability to promote innovation and collaboration between sectors.

Keywords: Power and Eneergy Systems
Abstract: It is known that uncertainties in model input parameters are often neglected in electromagnetic transient studies due to the difficulty of determining these parameters. Furthermore, it is common to question the importance of each parameter in the model and the impacts on the results when using these parameters as typical. To overcome the exposed problem, this paper presents the principles and potential applications of parametric analysis in electromagnetic transient studies. For this, two methodologies are presented, which share different computational tools, but had almost identical results.

Keywords: Industry Data Science Applications, Big Data in Industry Applications, Diagnosis, Prognosis and System Identification
Abstract: Condition-based Maintenance (CBM) has proven effective in minimizing unnecessary downtime by scheduling maintenance actions only when indicators suggest machine health degradation. However, much of the existing CBM literature focuses on classification algorithms using benchmark or static datasets, leaving a gap in real-time modeling with real-world data model evaluation. This study addresses this gap by proposing a Bayesian Regression Model combined with the Bayesian Online Change-Point Detection (BOCD) algorithm, applied to regression residuals, as a heuristic for modeling machine states and transitions based on real-time vibration and temperature signals. The approach is based on two hypotheses: (i) the Bayesian regression model captures the current machine state, and (ii) residual-based change-point detection identifies critical state transitions with enough time for a machine maintenance or replacement action. Validation is performed using a proprietary dataset from a Brazilian startup, collected through battery-powered sensors monitoring vibration and temperature from rotary machines. Results confirm the effectiveness of the proposed method, demonstrating its promising application for the CBM scenario as an accurate real-time machine condition monitoring.

Keywords: Automation and Process Control, Internet of Things
Abstract: Efficient inventory management plays a vital role in maintaining the continuity of operations and reducing financial losses across a wide range of industries. As businesses increasingly rely on streamlined supply chains and just-in-time delivery systems, the ability to accurately monitor stock levels and respond promptly to inventory fluctuations has become essential for sustaining productivity, meeting customer demand, and remaining competitive in dynamic markets. This paper presents the development of a cost-effective RFID- and MQTT-based stock-out detection system designed for real-time inventory tracking in retail environments, particularly for high-value electronic devices. The proposed system integrates the ESP32 microcontroller with an RFID reader module to automate the identification and monitoring of items in inventory. Real-time updates are achieved through MQTT communication, publishing item status to predefined topics for easy integration with external systems. The hardware design includes a custom-printed circuit board (PCB) and a mechanical enclosure optimized for durability and functionality. The firmware, developed in the Arduino IDE, enables efficient Wi-Fi connectivity and supports real-time messaging, minimizing human intervention. With a manufacturing cost of approximately USD 19.61, the system is both scalable and accessible for small and medium-sized businesses. Experimental results demonstrate the system’s ability to reliably detect and publish the presence or absence of inventory items, showcasing its potential as a lightweight and adaptable solution for inventory management. Future improvements, such as a user-friendly configuration interface, are proposed to further enhance the system’s usability and scalability.

Keywords: Smart Grids
Abstract: Em uma área de distribuição local (ADL), o operador do sistema de distribuição (OSD) é o agente responsável tanto por realizar o procedimento que toma as decisões otimizadas para o sistema de distribuição (mercado varejista), quanto por calcular os lances a serem submetidos ao mercado atacadista, que ocorre entre os agentes conectados na transmissão, cujo coordenador é o operador de sistema de transmissão (OST). Na literatura são apresentadas duas formas de interação para que o OSD realize as duas tarefas, a interação passiva e interação ativa entre OSD e OST. Na primeira, o OSD assume que não tem poder de alterar os preços de equilíbrio do mercado atacadista, enquanto que na segunda, o OSD assume que a flexibilidade de demanda da ADL, em função de seus recursos de energia distribuídos (REDs), podem alterar os preços do mercado atacadista. Neste trabalho investigou-se modelos propostos na literatura através de uma metodologia desenvolvida para avaliar a qualidade dos lances calculados pelo OSD e concluiu-se que a interação ativa, que leva em conta no modelo as opções do OST, proporciona o cálculo de lances com preços mais ajustados à demanda real da ADL e um despacho mais eficiente dos REDs.

Keywords: Optimization Heuristics and Methods, Personalized Products, Industry Applications
Abstract: This paper presents a framework for optimizing and evaluating magnetic field intensity and uniformity. The focus is on research and equipment developing to combat the fouling problem. In this study, Optuna's NSGA-II genetic algorithm and a custom-made magnetic field simulator are employed to optimize magnetic equipment parameters for fouling prevention. The simulated scenario consists of a tube surrounded by six equally spaced current rings that generate a magnetic field. The optimization parameters include the current intensity in each individual ring and their distance from the center of the tube. Optimized parameters are the average intensity of the magnetic field and its standard deviation. From the optimized parameters, while the second aims to ensure greater uniformity by being minimized, the first is maximized to ensure the effect of the magnetic field. The results reveal an optimal relationship between the rings' currents to balance both objectives, with the distance from the sources to the center of the tube being a determining factor in the resultant Pareto front. These findings consolidate the method as an effective tool for optimizing the evaluated magnetic equipment.

Keywords: Electrical Machines and Drives, Electrical Vehicle and Energy Storage
Abstract: This paper proposes an analytical-numerical methodology for designing PMBLDC motors with a focus on traction of an electric scooter. The analytical calculation was prepared in such a way as to guarantee an entreferro induction with sufficient value for proper magnetization. After that, the inductions in the other parts of the machine were calculated in such a way as to avoid saturation of the ferromagnetic material as well as producing repulsion and attraction between the stator and rotor poles. As a next step, two magnetic simulations were carried out, one in the 2-D dynamic and one in the 3-D transient regimes, to compare to the values obtained analytically and it was found that the values were compatible. Due to its use in the wheel, the internal motor temperature was investigated, assuming a external temperature. Based on the power dissipated and the external temperature, a thermal simulation was carried out to check the temperature levels reached in the permanent magnets and in the armature conductors. To validate this methodology, an existing prototype was used to compare the motor's dimensions, as well as two FEM numerical resolution software packages.

Keywords: Resource Efficienty & Circular Economy Tracking, Power Quality, Renewable Energy
Abstract: This paper evaluates the energy efficiency in electricity generation and consumption across the states of the Brazilian Legal Amazon using Data Envelopment Analysis (DEA). Given the region's critical role in Brazil's energy matrix and its vast socio-environmental diversity, assessing the efficiently with which states transform energy inputs into economic outputs is essential for guiding sustainable development strategies. The results indicate significant disparities, with only four of the nine states, Acre, Amazonas, Mato Grosso, and Roraima, attaining complete technical efficiency. The findings emphasize that high efficiency is not necessarily associated with large-scale production, but rather with a balanced relationship between energy use and economic return. By focusing on subnational units within a region of global ecological relevance, this paper provides original insights into the debate on regional energy transitions. It offers a valuable tool for policymakers to identify local bottlenecks and opportunities, supporting the formulation of targeted, evidence-based policies for a more equitable and efficient energy future on the Amazon.

Keywords: Optimization Heuristics and Methods, Internet of Things, Industry 4.0
Abstract: Most microcontrollers on the market come equipped with a Universal Asynchronous Receiver-Transmitter (UART) interface that provides a physical layer for communication between low-cost and low-power devices. However, the low complexity of the UART also makes it unreliable and very susceptible to errors. Notwithstanding, the application of a protocol that runs on the UART is important to ensure the reliability and effectiveness of communication, especially for the transmission of long messages and the execution of processes that require an uninterrupted sequence of commands. Hence, this work presents the development of a proprietary protocol to add reliability to UART communication between two microcontrollers, using structured messages and FreeRTOS tasks to receive messages. With this protocol, it was possible to detect and resend messages with integrity errors and message start detection in microcontrollers that communicate and run many other tasks in parallel.

Keywords: Smart Grids
Abstract: Understanding the structure of electricity consumption data and exploring possible differences between normal and fraudulent behaviors are important steps toward supporting future proposals for energy theft detection systems. In this article, an exploratory data analysis was conducted on two randomly selected consumers (one normal and one fraudulent) from the State Grid Corporation of China dataset, one of the most widely used datasets for studying electricity theft. The analysis aimed to uncover latent consumption patterns by applying the autocorrelation function, correlation matrices, Shannon entropy analysis, weekly boxplot analysis, and periodograms to the two consumers described above. Additionally, the silhouette index was computed over the entire dataset to assess cluster cohesion and structure. Dataset labels were used solely for comparative analysis, with no intention of training or evaluating classification or prediction models. While the analysis is limited to a small sample, initial results suggest that, in this specific case, the fraudulent profile exhibited lower variability, weaker temporal and spectral structures, and lower entropy as well. The silhouette analysis showed a weak intra-cluster cohesion for the fraudulent consumers. These preliminary findings, though not generalizable, may inform future approaches in preprocessing, feature engineering, and anomaly understanding in smart grid contexts.

Keywords: Artificial Intelligence, Electrical Machines and Drives, Power Electronics
Abstract: Este artigo apresenta o desenvolvimento e a implementação, em hardware digital, de um controlador baseado em lógica fuzzy tipo 2 (FLC2) para uso em um sistema de rastreamento do ponto de máxima potência – MPPT, que acopla os painéis fotovoltaicos às cargas, visando reduzir os efeitos causados pela intermitência na geração de energia, sejam eles causados por condições ambientais ou de carga. Portanto, visa maximizar a eficiência do sistema de geração de energia por sistemas fotovoltaicos. O MTTP baseado no FLC2 é capaz de diferenciar a tensão de operação do painel fotovoltaico (PV) e buscar o ponto de máxima potência que o PV pode produzir. Devido à estrutura complexa do controlador proposto, o uso de um Field Programmable Gate Array (FPGA) é atrativo para sua implementação, pois permite a execução de processamento paralelo. O controlador foi desenvolvido utilizando a linguagem de descrição de hardware – VHDL, e sintetizado no FPGA EP3C16F484-C6 disponível na placa DE0. A eficácia da solução foi comprovada por meio dos testes e medições em bancada realizados com o protótipo.

Keywords: Industry Applications, Industry 4.0, Automation and Process Control
Abstract: The main objective of this article was to perform a bibliometric analysis on the topics: Asset Management and Industry 4.0. To achieve this objective, some selection and refinement criteria were applied to the list of documents obtained through extractions from scientific databases (Scopus and Web of Science), following methodologies adopted in previous studies of a similar nature. From the final sample obtained, graphs and tables were prepared, which support the analyses performed. The results offer, from a panoramic view of the intersection between the two topics in question; through the identification of the authors, origins and articles that most contribute to the growth of related research; reaching the identification of possible opportunities for future research. Thus, it is expected that this study will serve as a starting point for subsequent investigations, which deepen the articulation between Asset Management and Industry 4.0, focusing on the development of new approaches or new concepts.

Keywords: Power and Eneergy Systems
Abstract: This paper presents a systematic approach that simplifies the process of deriving the dual problem associated with a large-scale linear optimization problem (primal). The wholesale energy market auction problem is adopted as an illustrative example of dualization. This problem aims to maximize the social welfare function of the day-ahead electricity market, subject to the system’s economic, physical, and operational constraints. Based on the day-ahead auction model, a step-by-step procedure is described to obtain the corresponding dual problem. This procedure primarily involves constructing the Lagrangian function, from which the dual function and the dual constraints can be derived. The solutions to the primal and dual problems are compared in light of optimization theory, highlighting their intrinsic relationship. Both the primal and dual models were implemented using GAMS software. The results demonstrate consistency between the primal and dual solutions, as predicted by optimization theory.

Keywords: Diagnosis, Prognosis and System Identification
Abstract: Piezoelectric transducers are widely used in structural health monitoring (SHM) systems due to their ability to convert mechanical energy into electrical energy and vice versa, establishing an electromechanical coupling with monitored structures. In this research, the technique used is based on the analysis of the frequency response function (FRF), with the objective of evaluating and comparing the efficiency of different excitation signals (chirp, burst, sinusoidal, impulse and white noise) in the detection of structural damage. The tests were carried out on aluminum and carbon fiber-reinforced plastic (CFRP) structures, where structural damage was induced by adding mass. In addition to the evaluation of the different types of signals, the effects of amplitude and frequency range were also analyzed, comparing them to the root mean square deviation (RMSD) and correlation coefficient deviation metric (CCDM) damage indices. The results indicate that the chirp and sinusoidal signals provided greater sensitivity in the detection of damage, standing out as the most efficient in both aluminum and CFRP structures.

Keywords: Automation and Process Control, Electrical Machines and Drives, Industry 4.0
Abstract: Este texto aborda e aplica um controle ativo de rejeição de distúrbios (active disturbance rejection control, ADRC) linear, empregando um integrador generalizado no observador de estados estendidos (Generalized Integrator-Extended State Observer, GI-ESO), para regular a posição do rotor de uma máquina de indução sem mancais. A máquina foi submetida a testes de variação de referência em degrau, bem como a aplicação de carga para avaliação da resposta do controlador. Os resultados mostraram que o tipo de controlador utilizado responde às diferentes variações aplicadas, estabilizando o rotor.

Keywords: Artificial Intelligence
Abstract: The rapid generation of complex data in the digital health landscape, characterized by Big Data, presents significant challenges for conventional analysis methodologies. However, this massive volume of information simultaneously offers a crucial opportunity for the application of Artificial Intelligence (AI), with the potential to radically transform diagnostic, monitoring, and treatment processes for various diseases. This research aims to study and analyze technological scenarios for Digital Health, focusing on the application of AI in diagnosis, monitoring, and personalized treatment. The methodology involves a comprehensive analysis of the state-of-the-art applications of AI in medical diagnosis, patient monitoring, and personalized treatment within the context of the digital transformation in health services. The study identifies key opportunities and challenges for the widespread implementation of AI-based solutions in the healthcare sector, proposes innovative solutions integrating AI and telemedicine, and discusses the future trajectory of AI in healthcare. The findings highlight significant advancements in AI applications for medical diagnosis, patient monitoring, and personalized treatment development, driven by Machine Learning (ML) and Deep Learning (DL) techniques. Despite the promising potential, ethical, legal, and technical challenges must be addressed to ensure safe and effective AI adoption in healthcare. The study concludes that robust strategic planning and adequate investments can enable Brazilian hospitals to achieve effective digital transformation, resulting in significant improvements in healthcare service efficiency and quality.

Keywords: Diagnosis, Prognosis and System Identification, Industry Applications, Automation and Process Control
Abstract: Falhas em instrumentos de medição de variáveis críticas nas plantas industriais podem provocar paradas de produção, perdas materiais e, em casos extremos, acidentes graves. Por isso, a detecção precoce dessas falhas é essencial para garantir a segurança e a confiabilidade dos processos. Este artigo compara diferentes técnicas para detecção de falhas aplicadas a sensores de temperatura em fornos de pelotização, com foco em soluções que possam ser implementadas em sistemas industriais com restrições de processamento, como controladores lógicos programáveis e sistemas de controle distribuído. Foram avaliadas abordagens como média móvel exponencial, desvio padrão exponencialmente ponderado, filtro de Kalman e os observadores de Luenberger e H_Infinito. Os observadores apresentaram F1-score superiores a 98%, porém com tempos médios de processamento elevados, 600 ms para o Luenberger e 724 ms para o H_Infinito. O desvio padrão exponencialmente ponderado obteve F1-score de 96,21% com tempo médio de processamento de apenas 2,6 ms, enquanto a média móvel exponencial atingiu um F1-score de 91,60% com tempo de 2,9 ms, demonstrando maior aderência aos requisitos exigidos para aplicações em ambientes industriais.

Keywords: Renewable Energy, Power and Eneergy Systems, Power Quality
Abstract: Innovation in energy applications increasingly demands control strategies that enhance the adaptability and efficiency of renewable energy systems. Wave energy converters (WECs), operating in stochastic and highly dynamic marine environments, require the capacity to adjust key system parameters in response to changing sea states to optimise performance and durability. This study proposes a feedforward predictive stiffness adaptation methodology for a WEC, employing polynomial Nonlinear AutoRegressive Moving Average with Exogenous Inputs (NARMAX) models. By forecasting future system dynamics, the method enables the adjustment of the power take-off (PTO) stiffness according to predicted mechanical energy levels, following a threshold-based policy. Numerical simulations on the Reference Model 3 (RM3) WEC under four representative sea states illustrate that the approach achieves reductions in average stiffness of up to 47% relative to the nominal setting. The results demonstrate the potential of predictive adaptation techniques to support renewable energy operations through enhanced system resilience and sustainable performance.

Keywords: Industry 4.0, Internet of Things
Abstract: Open hardware may enhance the transparency of hardware solutions for security critical infrastructure in Industry 4.0. However, it is not enough to have all the related resources available in an open access repository, as an open hardware solution must also encompass ease of replication. The objective of this work is to empirically validate the synthesis procedure of the Precursor open hardware solution to identify its limitations such as lack of updated synthesis instructions and less community support when compared to an open software solution. Given the threat of hardware trojan insertion by malicious EDA tool, this work also investigated the possibility of using the Yosys synthesis tool within a IPython Notebook for facilitated replication. It was possible to perform the synthesis using the commercial Xillinx Vivado tool in a Linux Virtual Machine, but the open-source synthesis was still not supported for the Precursor System on a Chip (SoC) given its complexity.

Keywords: Diagnosis, Prognosis and System Identification, Deep Learning and Machine Learning, Artificial Intelligence
Abstract: Control valves are widely used in industrial processes and are therefore subject to various types of faults, ranging from simple to several. To address this, a fault diagnosis system based on an Extreme Learning Machine (ELM) is used to classify these faults. Using a mathematical model of an industrial hydraulic control valve, the input variables - force and fluid flow - and their corresponding fault category labels were used to train and test the ELM. The number of hidden layer nodes in the ELM was determined through iterative selection, and a sigmoid function was employed as the activation function. The training speed and classification accuracy achieved by the ELM are also presented.

Keywords: Electrical Machines and Drives, Power and Eneergy Systems, Industry Data Science Applications
Abstract: Power transformers are critical assets for system reliability, as they connect different voltage levels across generation, transmission, and distribution. Their failure can lead to significant impact in multiple aspects such as financial, social, and regulatory. In recent years, rising costs and extended lead times for replacements have posed additional challenges for utilities, increasing the need to extend the service life of the existing fleet. Continuous asset monitoring has emerged as a key enabler of data-driven asset management. By integrating online and offline data along with a comprehensive risk assessment, utilities can better evaluate a transformer fleet, optimize maintenance strategies, and support strategic decisions regarding repair, replacement, or investment in monitoring technologies. This work discusses the parameters used for power transformer condition and risk assessment and presents a framework for a tool designed to support resource allocation and investment prioritization in power transformer fleet asset management.

Keywords: Diagnosis, Prognosis and System Identification, Power and Eneergy Systems, Power Quality
Abstract: A crescente demanda por energia elétrica impõe desafios significativos à confiabilidade dos sistemas de transmissão e distribuição, tornando a integridade dos cabos isolados um ponto crítico. Este trabalho realiza uma revisão técnica aprofundada sobre a detecção e análise de descargas parciais (DPs) como ferramenta diagnóstica para a avaliação da condição do isolamento desses cabos. Explora-se a natureza das DPs, suas classificações (internas, superficiais, corona) e o impacto de fatores estressores no envelhecimento do isolamento. O artigo detalha as diversas metodologias de medição, tanto em regime online quanto offline, e aborda as técnicas elétricas, eletromagnéticas, acústicas e ópticas empregadas. Discute-se a importância de parâmetros de caracterização das DPs, bem como os desafios e soluções para a interpretação de sinais em ambientes ruidosos. O objetivo é fornecer um panorama consolidado das abordagens para o monitoramento e diagnóstico de cabos isolados, visando aprimorar as estratégias de manutenção e a segurança operacional.

Keywords: Renewable Energy
Abstract: The detection of microcracks in solar panels is crit- ical for maintaining their efficiency and longevity, particularly in industrial-scale solar farms. Microcracks, which often form due to hail impact, are imperceptible to the naked eye and can- not be identified through conventional visual or drone inspec- tions. Electroluminescence (EL) imaging is required to capture these microcracks, as it enables the visualization of internal defects in photovoltaic (PV) cells. However, current inspection processes rely heavily on manual analysis of EL images, which is time-consuming and labor-intensive. This study proposes an automated computer vision-based approach for detecting and classifying microcracks in solar panels using EL imaging. The model is trained and evaluated on a newly developed dataset comprising EL images captured after controlled hail impact tests conducted at the research campus of Factory Mutual (FM). Despite the modest dataset size, our deep learning-based model achieves high detection performance, demonstrating the viability of accurate microcrack analysis under data-limited conditions. Furthermore, we show that targeted data augmen- tation techniques substantially improve model robustness and generalization. The proposed method provides a scalable and efficient alternative to manual inspection, contributing toward the broader goal of automated and reliable defect detection in PV systems.

Keywords: Renewable Energy, Smart Grids
Abstract: This paper evaluates the potential of green hydrogen production based on solar and wind energy in the state of Pará, Brazil. The analysis models a solar and wind energy system, and electrolyzers to convert renewable energy into hydrogen, based on available solar irradiance and wind speed. The meteorological database used are Wind and Solar Energy Atlases public available at official government platforms from Brazil. A geospatial analysis is developed in QGIS to create thematic maps and identify regions optimal for green hydrogen generation. Economic viability is assessed through the calculated leveled cost of the produced hydrogen. The results show higher potential for green hydrogen production based on solar energy, covering a significant part of the state with production capacity between 39.79 kg/h and 47.29 kg/h. Green hydrogen from wind energy is restricted to some specific locations on the coast, reaching maximum production values of 19.78 kg/h.

Keywords: Renewable Energy, Power and Eneergy Systems, Smart Grids
Abstract: The penetration of Distributed Energy Resources (DER) in power systems has been driving transformations in the electrical sector and paving new pathways for renewable resources integration in different industries. In this context, microgrids are well-known for their flexibility and control capabilities for systems in which small-scale and intermittent generation is significant. Further, hydrogen was identified as a promising asset for electricity production or indirect storage. In contrast, a less frequently discussed alternative is the application of distributed renewable generation for obtaining green hydrogen (GH2) from water electrolysis. This GH2 would be applicable in different industries, such as automotive or chemical. Given this background, this paper evaluates logical and dynamic control strategies for microgrids aimed at GH2 production, prioritizing local renewable energy to power electrolyzers directly. An overview of microgrid and GH2 concepts is presented, aspects regarding power systems dynamic modeling are highlighted, and the PSCAD environment is applied for simulations. A real microgrid under development is taken as a reference, incorporating photovoltaic generation, electrolyzers, battery storage, and intelligence layers. Results demonstrate differences in responsiveness and energy balance performance for equivalent on-grid and off-grid scenarios and encapsulate interactions between controls and hydrogen production, emphasizing the importance of system-level coordination.

Keywords: Machine Learning, Life Support Systems & Techniques, Diagnosis, Prognosis and System Identification
Abstract: This study explores the application of machine learning algorithms to predict the survival of patients with heart failure, a critical condition responsible for high global mortality. Using the public Heart Failure Clinical Records dataset, which contains clinical information from 299 patients, 13 variables were analyzed, including ejection fraction, serum creatinine, and age. Three predictive models were employed: Logistic Regression, Random Forest, and Multilayer Perceptron (MLP). Additionally, evaluation metrics such as the confusion matrix and the ROC curve AUC were used. The results indicated that ejection fraction and serum creatinine are the main predictors of mortality, with the Random Forest model being the most effective, achieving an AUC of 0.79, followed by MLP and Logistic Regression, both with an AUC of 0.74. The Kaplan-Meier curve confirmed the trend of declining survival over time. It is concluded that the use of machine learning is promising for improving prognostic accuracy and supporting clinical decision-making based on a reduced set of relevant variables.

Keywords: Industry 4.0, Big Data in Industry Applications, Cybernetic Systems
Abstract: This study explores the digital transformation of Brazil’s Unified Health System (SUS) through an integrative literature review. The aim was to identify the main thematic axes addressed in national academic publications and to analyze the scope, advances, and limitations of digital health strategies over the past decade. A search was conducted in the Virtual Health Library (BVS) LILACS Plus collection, using a structured query that resulted in 52 documents, from which 25 were selected for full analysis. Four major thematic axes were identified: (1) digital health and telehealth, (2) education and training, (3) information management and public policies, and (4) digital exclusion. The findings reveal that while the SUS has made significant progress in adopting digital tools – particularly during the COVID-19 pandemic – numerous challenges remain. These include structural inequalities, digital illiteracy, lack of interoperability, and the need for participatory governance and inclusive policies. The review highlights the importance of integrated strategies that combine technological, educational, and ethical approaches to ensure equitable digital health transformation

Keywords: Power Electronics, Industry Applications
Abstract: Planar-core transformers with a low height profile are now being used in compact converters operating at high switching frequencies because they have characteristics that overcome the limitations of conventional magnetic components, including high power density, high reliability, excellent thermal stability and reduced leakage flux. In order to benefit from these advantages, the choice of suitable materials for magnetic core, with lower losses due to hysteresis and parasitic effects, is necessary, together with careful analysis of the strategy for positioning the windings and sizing the conductors, in order to mitigate losses due to the proximity between the coils and the skin effect of the current at high frequency, respectively. In this work, a comparative analysis was carried out between two methodologies of sizing for specific application in development of low-power flyback DC converters operating in the 100kHz frequency range. Based on the results obtained with the analytical methods, the structure with the best performance was modeled using computational tools using the finite element method.

Keywords: Artificial Intelligence, Deep Learning and Machine Learning, Industry Applications
Abstract: A medição precisa do desgaste em bocais de lança de conversores LD/BOF é fundamental para a manutenção preditiva e a otimização do processo de refino de aço. Métodos tradicionais de inspeção enfrentam desafios de precisão e variabilidade nas condições de captura das imagens. Este trabalho propõe uma abordagem integrada que utiliza a rede neural YOLOv11 para detecção automatizada de desgaste, combinada com técnicas de processamento digital de imagens (PDI) e lógica Fuzzy para aprimorar a qualidade das imagens de entrada. A lógica Fuzzy adapta dinamicamente parâmetros de imagem, como brilho e contraste, enquanto o PDI mitiga os efeitos da iluminação variável, proporcionando uma análise mais robusta. A metodologia foi validada experimentalmente em laboratório, mostrando que a combinação de YOLOv11, PDI e lógica Fuzzy oferece resultados superiores em termos de precisão e confiabilidade em comparação com abordagens convencionais. Os resultados indicam que o sistema proposto pode ser uma solução eficaz para a automação da medição de desgaste em ambientes industriais.A medição precisa do desgaste em bocais de lança de conversores LD/BOF é fundamental para a manutenção preditiva e a otimização do processo de refino de aço. Métodos tradicionais de inspeção enfrentam desafios de precisão e variabilidade nas condições de captura das imagens. Este trabalho propõe uma abordagem integrada que utiliza a rede neural YOLOv11 para detecção automatizada de desgaste, combinada com técnicas de processamento digital de imagens (PDI) e lógica Fuzzy para aprimorar a qualidade das imagens de entrada. A lógica Fuzzy adapta dinamicamente parâmetros de imagem, como brilho e contraste, enquanto o PDI mitiga os efeitos da iluminação variável, proporcionando uma análise mais robusta. A metodologia fo

Keywords: Industry 4.0, Cyber Physical Systems, Digital Twins and Knowledge Systems, Internet of Things
Abstract: The evolution of Industry 4.0 has driven the modernization of industrial processes, promoting greater digitization and automation across various sectors. However, the replacement of legacy systems with technologies more compatible with this new scenario can pose a significant challenge, especially for small and medium-sized enterprises (SMEs), due to resource constraints. This work explores the use of the Asset Administration Shell (AAS) as a solution for monitoring and controlling industrial processes, taking as a use case the temperature control in an NVPT test bench, representative of legacy systems. The architecture was structured based on the RAMI 4.0 reference model, ensuring compliance with Industry 4.0 principles. The adopted approach consists of system virtualization through a reactive AAS responsible for temperature regulation. For this purpose, the AAS will have a PID controller implemented using the Python language with the simple-pid library. The AAS was developed using the AASX Package Explorer and implemented in Python with the opcua asyncio library, enabling communication via OPC UA. The execution was performed on a Raspberry Pi, leveraging the advantages of edge computing to ensure greater efficiency and autonomy in data processing. The results indicate that the methodology employed enabled the digitization of the test bench, facilitating remote monitoring and automated control of system variables. Furthermore, the tests carried out confirm the feasibility of the proposal, highlighting its effectiveness in modernizing legacy industrial systems in an accessible manner aligned with the demands of Industry 4.0.

Keywords: Electrical Vehicle and Energy Storage, Power Electronics, Renewable Energy
Abstract: This paper proposes a modeling and control strategy for a three-phase T-Type rectifier based on phase disposition PWM in the dq0-reference frame. This approach enables the use of conventional PI controllers while ensuring zero steady-state error for sinusoidal signals at the fundamental frequency. The proposed control strategy achieves high dynamic performance and facilitates implementation, making it suitable for practical applications. The T-type topology is a promising solution for high-power-density bidirectional threephase AC-DC converters, particularly in electric vehicle charging systems. A static analysis is provided, encompassing input filter sizing, control strategy, and computational implementation. Additionally, a resistive load is employed at the output of the first stage to emulate the second stage of the vehicle charger, representing a DC-DC converter responsible for managing the charging process of a battery bank. The application of topology is validated through computational simulation results, obtaining a maximum THDi of 1.22% under different power rates varying from 50% to 100%.

Keywords: Artificial Intelligence, Deep Learning and Machine Learning, Internet of Things
Abstract: Este artigo apresenta o desenvolvimento de um protótipo de sistema de automação residencial para o controle de acesso a residências por meio de reconhecimento facial utilizando a linguagem de programação Python e a plataforma open source Arduino.

Keywords: Additive Manufaturing, Industry 4.0, Self Configuration & Self Diagnosis
Abstract: This study investigates the feasibility of retrofitting an obsolete 3D printer through the implementation of Klipper firmware, an open-source system designed to extend the operational life of the machine and align it with current technological demands. The research methodology combined a theoretical foundation derived from a literature review with an analysis of technical specifications from hardware and software manufacturers, followed by a virtual simulation using VirtualBox and a practical implementation on a FDM printer equipped with a Raspberry Pi 4 Model B and a Bigtreetech Octopus v1.1 control board. The retrofit process included the installation of the Raspbian operating system, configuration of the firmware via the KIAUH script, and detailed adjustments to the printer.cfg file to ensure proper integration of motors, extruders, and sensors. The results confirmed a successful modernization of the equipment, introducing features such as remote access, a graphical user interface, and potential for future scalability, which collectively enhanced the machine’s performance. The discussion highlights the adaptability of Klipper and the interdisciplinary expertise required for its implementation, while also acknowledging challenges related to network infrastructure dependency and technical complexity. The findings support the conclusion that retrofitting with open-source technologies offers a cost-effective and sustainable approach to upgrading 3D printers, particularly within the context of Industry 4.0, as long as it is accompanied by appropriate training. Future research may explore the integration of advanced modules and the broader application of this technique, further emphasizing the critical role of open-source solutions in additive manufacturing.

Keywords: Power Electronics, Renewable Energy, Power and Eneergy Systems
Abstract: The increasing penetration of Inverter-Based Resources (IBRs) into modern power systems has raised concerns regarding their impact on protection schemes, particularly distance protection. In this context, this paper evaluates the influence of different Phase-Locked Loop (PLL) architectures on the reliability of self-polarized and memory-polarized distance protections in systems with Grid-Following IBRs. The effects of conventional, proportional, and blocked PLL structures are assessed through extensive simulations in a transmission system with a typical topology for interconnecting Full-Converter IBRs to the grid. The study reveals that improper PLL operation leads to circular variations in impedance trajectories, boosting underreach and overreach issues. Among the analyzed structures, the blocked PLL yielded the most stable results, mitigating the adverse effects on distance protection performance. However, the literature lacks standardized methodologies for effective PLL blocking, highlighting the need for further investigations. The findings emphasize the necessity of in-depth studies on IBR control strategies to ensure reliable protection system operation in future power grids.

Keywords: Bioprocessess Applied to Industry, Optimization Heuristics and Methods, Deep Learning and Machine Learning
Abstract: This study investigates multi-objective optimization of single-screw extruders for aquafeed production, focusing on extrudate temperature, cooling capacity and extrudate density. Using a single-screw extruder with an integrated countercurrent water cooling system, the study determined optimal process conditions through Response Surface Methodology (RSM) and Multi-Criteria Decision-Making (MCDM) analyses. The Pareto front revealed trade-offs between objectives, with an optimal point achieved at a screw speed of 258.82 rpm, a die size of 8 mm and an extruder temperature of 100 °C, balancing product quality, energy efficiency and density. Experimental validation was performed using a 40–45% moisture content aquafeed mixture. RSM and MCDM predictions were compared with experimental results. MCDM outperformed RSM in terms of cooling performance and cooling temperature, with predictions of 65.25% and 122.45 °C closely matching the experimental values of 70.2% and 127.3 °C, respectively. Conversely, RSM was more accurate in predicting extrudate density, with a prediction of 0.34 kg/m³ compared to MCDM's 0.25 kg/m³. Statistical analysis showed that MCDM achieved a mean absolute percent error (MAPE) of 5.7% and an R² of 0.94, highlighting its robustness for multi-objective scenarios. The results highlight the complementary strengths of RSM and MCDM and support their integration for robust optimization of industrial extrusion processes.

Keywords: Life Support Systems & Techniques, Machine Learning, Diagnosis, Prognosis and System Identification
Abstract: Faced with the challenges of emotional development and mental health in adolescence, technology has emerged as a complementary resource to comprehend and promote strategies for emotional regulation, empathy and affective state, as well as to analyze the impact of family climate on these processes. This study presents the concept of emotional modulation, constructed in analogy to the concept of modulation in telecommunications engineering, as a model for assessing and fostering socio-emotional competencies in adolescents. Based on data from 114 adolescents, an algorithm was developed using the Chain Support Vector Machines (Ch-SVM) technique, which classifies levels of emotional competence and empathy. The model, validated using the 5-FOLD cross-validation method, showed an accuracy of 92%, precision of 89%, recall 89% and F1-score of 89%. This new approach to the science of emotions and computer science will help to provide innovative tools in the future that can be used both by education professionals in the development and promotion of the emotional well-being of adolescents and by mental health professionals in the therapeutic approaches for adolescents.

Keywords: Robotics and Mechatronics, Automation and Process Control, Industry Applications
Abstract: This paper presents the synthesis of a robust H∞ controller for high-speed unmanned underwater vehicles using linear matrix inequalities (LMIs). The model incorporates hydrodynamic uncertainties, considering variations in the cavitation coefficient and fin efficiency, ensuring stability through D-stability in a circular region. The robustness of the controller is evaluated by analyzing the feasibility of LMIs for different configurations of the dynamic parameters. Simulations demonstrate the effectiveness of the proposed approach, ensuring system stability and appropriate dynamic behavior even under parametric variations, validating its applicability in adverse underwater operations.

Keywords: Automation and Process Control, Electrical Machines and Drives, Industry Applications
Abstract: Este trabalho descreve o desenvolvimento de uma plataforma de controle de baixo custo para máquinas de ensaio de torção, com a capacidade de controlar torque ou deslocamento angular. Além disso, foi desenvolvido um software funcional e intuitivo para a operação do equipamento. Para apoiar o desenvolvimento, foi criada uma máquina de torção de baixa capacidade que servirá como plataforma de teste. O controlador opera em malha fechada, permitindo a seleção entre controle de torque e deslocamento, além da configuração de diferentes parâmetros como velocidade, direção e ganhos. Neste estudo é apresentado um trabalho comparativo entre resultados obtidos por meio do controlador desenvolvido e de um controlador comercial já consagrado no mercado (controlador de ensaios marca MOOG modelo STAC). Os resultados demonstraram que, apesar de apresentar um módulo de elasticidade inferior ao sistema comercial (1,48 N.m/° contra 2,39 N.m/°, em média), o controlador desenvolvido foi capaz de reproduzir os valores de torque de escoamento e ruptura, com variações inferiores a 5%, evidenciando sua viabilidade técnica para aplicações em ensaios de torção. Adicionalmente, o sistema foi testado em controle cíclico para que se pudesse melhor avaliar a influência da estimativa da posição por um filtro de Kalman no controle, o qual apresentou uma redução perceptível no ruído do sinal de controle.

Keywords: Bioprocessess Applied to Industry, Deep Learning and Machine Learning, Industry 4.0
Abstract: This study presents an approach for the automated detection and analysis of Daphnia magna in aquatic environments using Computer Vision and Deep Learning techniques, focusing on the YOLOv8 model. Recognized as sensitive bioindicators in ecotoxicological assays, Daphnia magna play a key role in assessing water quality in both laboratory and natural settings. The YOLOv8 model was trained using experimental bench data, with the organisms classified into three size categories: small, medium, and large. Data augmentation techniques were applied to address variations in lighting, population density, and background. Annotations were performed using the Roboflow tool, and the methodology was grounded in recent studies, such as Egídio (2023), enabling performance comparisons. The model achieved a mean average precision (mAP@50) of 0.85, mAP@50-95 of 0.35, and a recall of 0.70, indicating robust and consistent detection capabilities. Performance metrics, including confusion matrix and precision-confidence graphs, validated the effectiveness of the proposed approach. This work highlights the potential of YOLOv8 as a scalable tool for automated environmental monitoring in ecotoxicological assessments.

Keywords: Renewable Energy, Power and Eneergy Systems
Abstract: The rapid expansion of renewable energy in Brazil has led to increased curtailment in wind and solar power plants, affecting energy efficiency and grid stability. This study analyzes curtailment patterns across time and space, classifying restriction types and assessing their regional impact. Despite Brazil’s high renewable potential, there is a lack of comprehensive quantitative evaluations of curtailment. This research fills this gap by applying a data-driven methodology, analyzing over 10,000 GWh of curtailed energy in 2024. The methodology consists of three steps: (i) data acquisition and preprocessing, (ii) quantitative analysis, and (iii) interpretation of results. Findings indicate that Rio Grande do Norte had the highest wind curtailment (5,251 GWh), mainly due to reliability constraints (CNF). In comparison, Minas Gerais led in solar curtailment (842 GWh), dominated by energy imbalance (ENE). The findings underscore the need for grid expansion, enhanced forecasting, and optimized dispatch strategies to minimize curtailment, ensuring efficient integration while maintaining system stability and reliability.

Keywords: Electrical Vehicle and Energy Storage, Power and Eneergy Systems, Renewable Energy
Abstract: The need for diversification of the energy matrix in our contemporary society has become increasingly prominent, driven by global challenges such as climate change and the growing demand for sustainability. This study aims to present a comprehensive data analysis on two highly relevant topics: Photovoltaics (PV) Systems and Electric Vehicles (EVs) applied in Electric Power Systems. The research was conducted using a bibliometric methodology, which enabled a detailed overview of the current state of research in these fields. The data were extracted from references published in renowned scientific databases, such as IEEE Xplore and Scopus, ensuring the relevance and up-to-dateness of the collected information. The data were organized and converted into graphs to facilitate the interpretation of the results, allowing a clear and objective visualization of the identified trends. The data were classified according to specific criteria, such as year of publication, database, country of origin of the publication, and other relevant aspects. The conclusions of this study indicate that the topics of PV Systems and EVs have been receiving increasing attention in the field of technical-scientific research. Thus, this work contributes to the understanding of the development of these technologies and provides valuable insights for future studies and applications in the energy sector.

Keywords: Deep Learning and Machine Learning, Internet of Things, Diagnosis, Prognosis and System Identification
Abstract: 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 succeeded in various applications, 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.

Keywords: Industry 4.0, Internet of Things
Abstract: Abstract— This study analyzes the evolution of digital transformation in the Brazilian industry through the adoption of Industry 4.0 technologies by small, medium, and large companies (SMEs), based on data from the National Confederation of Industry (CNI) from the years 2016 and 2022. The research compares the levels of technological adoption, the perceived benefits, and the challenges faced, highlighting the unequal progress among companies of different sizes. The results indicate that, although large companies lead in technological implementation, small and medium-sized enterprises have shown significant progress. The study emphasizes the importance of public policies and targeted strategies to enhance digital maturity in SMEs.

Keywords: Industry 4.0, Industry Applications, Resource Efficienty & Circular Economy Tracking
Abstract: A latent difficulty of Lean practitioners is to face the challenge of abstracting the concept of waste when dealing with non-manufacturing contexts, such as office work and services. Terms like production - as in overproduction -, transportation, stock/inventory and defective are commonly regarded and interpreted as manufacturing concepts, and not by its generic meaning and etymology, opening a path for the rising of unnecessary variations and complications of TPS, such as Lean Office, Lean Healthcare, etc. The concept of the “seven wastes” in the context of manufacturing efficiency is, nowadays, widely known and diffused. However, it is now known that there were several interpretation errors and negligence in translation and transposition of the Japanese concepts by Western researchers and management authors inside the naturally convoluted process of knowledge transfer. Following this path of investigation, I have found that critical to the subject of promoting waste elimination, is the fact that first definitions do not include a list of seven types, nor any list whatsoever. Hence, the purpose of this article is to analyze two of the earliest, pre-Lean, official sources of the concept of waste in the context of TPS. Then, propose its use as a more effective definition, by demonstrating how it simplifies the waste identification and waste elimination processes beyond work or business type borders. The resource view of the concept of waste found in TPS removes the unintended barriers created by its industrial origins, and reinstate the universal management system perspective of Taiichi Ohno.

Keywords: Artificial Intelligence, Automation and Process Control, Industry Data Science Applications
Abstract: A transformação digital no Sistema Único de Saúde (SUS) constitui uma resposta estratégica à complexidade crescente da gestão pública em saúde. Este estudo tem como objetivo analisar os impactos das ações implementadas por meio do Programa SUS Digital, instituído pela Portaria GM/MS nº 3.232/2024. A pesquisa adota uma abordagem híbrida, integrando análise qualitativa de documentos normativos e técnicos com avaliação quantitativa de indicadores de maturidade digital, como o Índice de Critérios de Saúde Digital (ICSD). Os resultados apontam desigualdade estrutural entre macrorregiões brasileiras no que se refere à infraestrutura tecnológica, interoperabilidade dos sistemas e capacitação profissional. Embora barreiras operacionais e culturais ainda limitem a total incorporação de tecnologias digitais, foram identificados avanços relevantes, como a ampliação do acesso remoto a serviços, o uso de inteligência artificial para apoio ao diagnóstico e a consolidação de plataformas digitais como o Meu SUS Digital. Conclui-se que a efetividade das estratégias digitais depende de investimentos contínuos, formação técnica e políticas públicas orientadas pela equidade, inovação e proteção dos dados pessoais.

Keywords: Power Electronics, Deep Learning and Machine Learning
Abstract: This article presents an analysis of a Dual Active Bridge (DAB) converter operating with Single Phase Shift (SPS) modulation, in which a Reinforcement Learning (RL)-based controller is proposed using the advanced Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for output voltage regulation. This controller is compared to a traditional PI controller. The study is conducted through simulations in the Matlab®/Simulink environment, utilizing the PLECS Blockset along with thermal and semiconductor models provided by Onsemi. The results obtained allow for a comparison of both controllers in terms of reference tracking and disturbance rejection. The evaluated parameters include overshoot, settling time, rise time, and the ITAE (Integral of Time-weighted Absolute Error) index. The results show that the RL-TD3-based controller improves the dynamic performance of the system compared to the PI control, particularly in terms of response speed and overshoot reduction.

Keywords: Industry 4.0, Industry Data Science Applications, Deep Learning and Machine Learning
Abstract: Este artigo propõe uma abordagem eficiente para a detecção de vazamentos em plantas eletropneumáticos, utilizando algoritmos de aprendizado de máquina — Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), Extreme Learning Machine (ELM) e Echo State Network (ESN). Com base na aquisição de sinais de pressão em diferentes condições operacionais, as séries temporais foram segmentadas e processadas, extraindo de atributos discriminativos com baixo custo computacional utilizando a biblioteca TSFEL. Os modelos foram avaliados quanto à acurácia, tempo de inferência e viabilidade de implementação embarcada. Os resultados evidenciam que modelos como o ELM apresentam alto desempenho mesmo em dispositivos com recursos limitados, como o ESP32, combinando robustez com baixa latência. A investigação demonstra o potencial de soluções acessíveis e confiáveis para manutenção preditiva em ambientes da Indústria 4.0, com foco em inteligência embarcada e custo reduzido.

Keywords: Optimization Heuristics and Methods, Industry Data Science Applications, Cyber Physical Systems, Digital Twins and Knowledge Systems
Abstract: Public health systems often face operational inefficiencies, such as long patient queues, limited resources, and service delays. These challenges are particularly critical in Brazil’s Unified Health System (SUS), where growing demand and budget constraints heighten the need for effective management strategies. This paper examines the application of the Theory of Constraints (TOC) as a methodology to optimize healthcare processes by identifying and addressing system bottlenecks. A real case study in a University Hospital is presented, focusing on the scheduling and communication of consultations and imaging exams. Business Process Model and Notation (BPMN) is used to map workflows, and TOC’s Five Focusing Steps are applied to guide improvements. The analysis reveals recurrent problems, including the absence of clinical prioritization, ineffective patient communication, and technological limitations. The findings demonstrate TOC’s potential as a practical and scalable management tool capable of delivering rapid and sustainable efficiency gains. Future work includes expanding the approach to other healthcare processes and presenting measurable results from real-world implementations.

Keywords: Smart Grids, Industry Applications, Internet of Things
Abstract: Este trabalho apresenta o desenvolvimento e a avaliação de uma arquitetura híbrida de comunicação para monitoramento energético industrial, integrando os protocolos ESP-NOW e MQTT. A solução proposta permite comunicação local eficiente entre dispositivos secundários via ESP-NOW e transmissão remota dos dados para um sistema supervisório utilizando MQTT, reduzindo significativamente a quantidade de dispositivos conectados diretamente à rede Wi-Fi. Os resultados experimentais demonstram alta confiabilidade, com perda de pacotes inferior a 0.002%, e baixa latência na comunicação local. Embora a integração com MQTT, especialmente via broker remoto, introduza maior latência, o desempenho geral permanece adequado para aplicações industriais. A arquitetura mostra-se escalável e eficaz para a modernização de sistemas legados sem necessidade de substituição completa do hardware, suportando monitoramento em tempo real e tomada de decisão. Essa integração representa uma abordagem econômica e robusta para o monitoramento energético em ambientes da Indústria 4.0, facilitando a transformação digital e automação. Trabalhos futuros incluem otimizações no firmware e testes em larga escala.

Keywords: Power Electronics
Abstract: The growing presence of Distributed Energy Resources poses challenges for real-time monitoring, control, and integration into smart grids. Effective communication is essential to ensure interoperability and grid stability. This work presents a complete stack solution for DER converter control, combining power electronics hardware with secure MQTT-based communication and a user interface. A real-time controller manages the converter, while an application processor calculates and transmits electrical statistics over a TLS-secured MQTT. Server-side, a microservice architecture stores data in InfluxDB and visualizes it in Grafana. Bidirectional communication allows real-time control and monitoring, demonstrating the role of modern protocols in enabling scalable DER integration.

Keywords: Life Support Systems & Techniques, Personalized Products, Self Configuration & Self Diagnosis
Abstract: Objective: To identify and analyse studies on the application of Lean Healthcare to patient safety in healthcare services in Brazil. Methods: An integrative literature review was conducted using the PubMed, Web of Science, BVS, Embase, and SciELO databases. Results: Of the 178 articles identified, 11 met the inclusion criteria. The most frequently reported patient safety approaches involved initiatives focused on effective communication among healthcare professionals and the implementation of safe surgical practices. Other initiatives targeted medication safety—including prescription, administration, and usage—reduction of healthcare-associated infections, and correct patient identification. Conclusion: The most commonly used Lean techniques/tools were Value Stream Mapping (VSM), 5S, and Kaizen. The international patient safety goals most frequently addressed, as established by the World Health Organization, were Goals 2, 3, 4, 5, and 1, respectively.

Keywords: Optimization Heuristics and Methods, Industry Applications
Abstract: El presente estudio tuvo como objetivo evaluar el impacto de la implementación de la metodología 5S en una planta de producción textil de escala media, con énfasis en la reducción del tiempo improductivo. A través de un enfoque experimental con diseño antes-después, se aplicaron las cinco fases del mé-todo (clasificación, orden, limpieza, estandarización y discipli-na), y se analizaron indicadores operativos mediante estadísti-ca descriptiva e inferencial. Los resultados evidenciaron una reducción significativa del tiempo improductivo (de 147,39 a 100,53 minutos por jornada, p = 0,0013), así como un aumento del cumplimiento de los ítems de calidad del 24,14 % al 82,76 %. El tamaño del efecto (d = 1,11) confirmó un impacto considerable. Se concluye que la metodología 5S representa una herramienta segura para optimizar procesos, mejorar el entorno laboral y fortalecer una cultura de mejora continua en el sector textil manufacturero.

Keywords: Electrical Vehicle and Energy Storage, Power Electronics, Industry Applications
Abstract: This paper presents an operation assessment of an electric vehicle (EV)-applied Dual Active Bridge (DAB) converter integrating a planar transformer, adopting the Single Phase Shift (SPS) modulation. In this paper, the planar transformer's parasitic elements are considered, and the operation is analyzed through computational simulations. The results show that the SPS' angle control is able to adequately synthesize voltages and currents at both the primary and secondary sides of the transformer, providing a stabilized output voltage for the load and an effective power transfer. It is also evidenced that the parasitic elements decrease energy efficiency as the phase shift angle increases, although they do not significantly impact the steady state operation performance of the converter.

Keywords: Automation and Process Control, Industrial Lighting, Resource Efficienty & Circular Economy Tracking
Abstract: This paper proposes mathematical models that can be used in the performance optimization of light shelves in classrooms, aiming to maximize the average illuminance. The methodology is based on system identification, using techniques such as polynomial regression and the least squares method (LSM) to adjust parameters. The simulation was performed in a classroom at the Federal University of Espírito Santo (UFES), using software such as Analysis SOL-AR, 3D Sun-Path, Relux and MATLAB to collect illuminance data as a function of the inclination angle of the light shelves and validate the model. The result shows that the static models of degree 4 were the most suitable to predict the average illuminance, with an average of inclination angles in the established periods. The study concludes that the proposed approach is effective to optimize natural lighting in urban environments, reducing the dependence on artificial lighting and promoting energy efficiency.

Keywords: Optimization Heuristics and Methods, Automation and Process Control, Self Configuration & Self Diagnosis
Abstract: This work presents an approach for online Particle Swarm Optimization (PSO) applied to the self-tuning of a PID controller in a DC-DC buck converter with nonlinear load. Unlike traditional offline metaheuristic methods, the proposed strategy operates in real time on the plant, and analyzes the relationship between the total number of iterations (used as a cost metric) and control performance. The methodology introduces adaptive agent allocation, utilization of both rising and falling transients to avoid reset times, and a composite stopping criterion to halt optimization based on convergence and stagnation indicators. Extensive experimental analysis, involving up to 30 PSO agents and 30 independent runs per configuration, reveals a clear trade-off between solution quality (measured by the ITAE index) and optimization cost. Results indicate that configurations with 12 agents offer the best compromise between precision and computational efficiency, while higher agent counts yield marginal performance gains at significantly increased cost. This framework offers practical guidelines for implementing efficient real-time metaheuristic optimization in embedded control systems.

Keywords: Smart Grids, Resource Efficienty & Circular Economy Tracking, Renewable Energy
Abstract: Currently, on the global stage, managing and optimizing energy use through smart devices and alternative generation forms is increasingly necessary due to the constant growth in energy demand, the scarcity of fossil fuels, and polluting factors. Smart cities have demonstrated the potential to merge non-renewable and renewable energy resources, reducing environmental problems and optimizing operational costs. Furthermore, the management of rainwater harvesting within smart cities plays a crucial role in sustainable urban development. This article uses the city of Rio de Janeiro as a case study. It proposes the installation of photovoltaic plants in existing buildings alongside cisterns to capture Rainwater for non-potable purposes. This integrated approach aims to optimize water consumption and mitigate flooding issues in the city.

Keywords: Diagnosis, Prognosis and System Identification, Machine Learning, Artificial Intelligence
Abstract: Cardiopulmonary Exercise Testing (CPET) provides critical physiological data for diagnosing limitations across cardiac, pulmonary, and musculoskeletal systems, and detecting physiological alterations related to conditions such as metabolic syndrome. However, the lack of full time-series data in some clinical groups limits the scalability of machine learning-based diagnostic tools. This study enhances a classification framework based on Support Vector Machines (SVM) and wavelet-based feature extraction by generating synthetic CPET time-series to complement existing real datasets. Artificial signals for Pulmonary and Musculoskeletal limitations were generated using real patient-derived statistics (mean, standard deviation, slopes, and peak timing), ensuring that the synthetic data reflect true physiological patterns. These were combined with full time-series CPET data from real Healthy, Heart Failure, and Metabolic Syndrome subjects, forming a five-class classification model. Classification was conducted using SVM with three wavelet decomposition levels (MW3) and five levels (MW5). The best model achieved an accuracy of 93.60%, precision of 84.00%, recall of 84.55%, and F1-score of 84.14% using MW5.

Keywords: Industry Applications, Power and Eneergy Systems, Renewable Energy
Abstract: O Esquema Regional de Alívio de Carga (ERAC) é de grande importância para o Sistema Interligado Nacional e sua operação necessita ser confiável e segura, não devendo atuar incorretamente para eventos originados em sistemas de distribuição. A ocorrência de um ilhamento não-intencional também pode resultar na atuação incorreta do ERAC, caso a proteção anti-ilhamento do gerador distribuído (GD) não o desconecte antes da atuação da função de proteção de subfrequência (81U) do ERAC. Dessa forma, neste trabalho é realizada uma análise detalhada de uma atuação incorreta do ERAC na área de concessão da concessionária RGE, utilizando oscilografias registradas do evento em questão. A partir dessa análise, é proposto um método analítico para determinar de forma prática um ajuste para função de proteção de taxa de variação de frequência (81R) do GD, que resulte na desconexão do gerador antes da sensibilização da função de proteção 81U do ERAC. O método foi validado por meio de simulações no ATP, utilizando dados reais da RGE, modelo detalhado do GD e motores de indução trifásicos. Os resultados indicam que o ajuste correto da função 81R, respeitando os requisitos de suportabilidade do Módulo 3 do Prodist, pode evitar a atuação incorreta do ERAC causada por ilhamento não-intencional.

Keywords: Personalized Products, Optimization Heuristics and Methods, Industry 4.0
Abstract: A transformação digital do Sistema Único de Saúde (SUS) exige modelos de capacitação profissional que conciliem eficiência de processos e centralidade no usuário. Este artigo apresenta um framework metodológico híbrido baseado no Lean Thinking e no Design Thinking, implementado em um Ambiente Virtual de Aprendizagem (AVA). O estudo foi conduzido segundo o método Design Science Research (DSR), que possibilitou a formulação, desenvolvimento, implementação e avaliação de um artefato aplicável ao contexto do SUS Digital. O framework integra princípios Lean, ferramentas de melhoria contínua, práticas de Design Thinking e metodologias ativas, resultando em um modelo de aprendizagem escalável e sustentável. Os resultados apontam para ganhos em eficiência operacional, engajamento dos usuários e potencial de aplicação em políticas públicas de saúde digital.

Keywords: Internet of Things, Industry 4.0, Renewable Energy
Abstract: The production, transport and storage of green hydrogen has been growing in Brazil and around the world as it is a means of storing sustainable and clean energy, due to its production coming from the electrolysis of water using electrical energy from renewable sources. One of the important factors in the hydrogen production chain is safety, as it is an extremely flammable gas, which poses a risk of explosion if not handled well. This article deals with the development of an application for remote monitoring of environmental variables using Industry 4.0 technology, called the Internet of Things (IoT), where any leak or change in the environment will be identified by sensors and monitored locally and by multiple applications.

Keywords: Internet of Things, Industry 4.0, Personalized Products
Abstract: This paper presents the development of BovTag, a cattle identification and counting system designed with emphasis on low energy consumption and accessible cost. The system combines an ATmega328 microcontroller and an ESP32 with a 433 MHz radio frequency communication module, enabling remote identification of animals through a unique tag code. BovTag is implemented on a circular printed circuit board with 40 mm diameter, suitable for rustic settings that require robustness. Tests demonstrated the system’s effectiveness in reliably transmitting identification data in rural environments, even in the presence of natural obstructions. Energy consumption was optimized, which allows extended operation using a single low capacity battery. As a result, BovTag stands as a viable and low cost alternative to more expensive technologies, such as active RFID or GPS based systems. This study contributes to technological innovation in livestock management, particularly for small and medium sized farms, by promoting automation in herd handling.

Keywords: Automation and Process Control, Industry Applications
Abstract: Este trabalho apresenta um estudo de técnicas utilizadas para caracterizar, de forma estática e dinâmica, uma válvula do tipo globo controlada por um posicionador pneumático. Ao longo do trabalho, também foram analisados os efeitos das variações do valor percentual de abertura desse sistema em aplicações envolvendo o controle de vazão. Inicialmente, foram realizados ensaios estáticos, aplicando-se sinais do tipo degrau à entrada do sistema de controle do posicionador. Os resultados obtidos demonstram que o sistema apresenta variações de abertura da ordem de 0,3% em torno dos pontos de operação avaliados, e que a variação percentual da posição de abertura real da válvula afeta o controle de vazão de forma diferente dependendo do ponto de operação analisado, com variações da ordem de 14 l/h no pior caso. Na sequência, foram realizadas análises dinâmicas, utilizando como referência para o posicionador sinais de controle senoidais, com frequências variando entre 5 mHz e 40 mHz, e sinais chirp, com variação linear de frequência entre 5 mHz e 40 mHz. Os resultados obtidos demonstraram uma redução do valor percentual máximo de abertura da válvula em função do aumento da frequência do sinal de referência aplicado à entrada do posicionador, chegando a 7% para sinais de 40 mHz, e um aumento da defasagem entre os sinais de controle aplicados ao posicionador e os valores percentuais de abertura reais da válvula, numa taxa de 3,7°/mHz.

Keywords: Renewable Energy
Abstract: O desenvolvimento das atividades humanas está profundamente atrelado ao consumo de energia, e essa relação tem se intensificado com o passar dos anos, resultando em uma demanda crescente por fontes energéticas. Este estudo visa explorar o potencial da energia hidrocinética, uma forma de geração renovável ainda pouco utilizada no Brasil, ao avaliar sua viabilidade no Rio Paraná, especialmente no trecho abaixo da Usina Hidrelétrica Engenheiro Sérgio Motta em Rosana - SP. O estudo pretende utilizar os dados coletados da Agência Nacional de Águas (ANA) na estação fluviometrica do Porto Primavera para medir o potencial energético da correnteza do rio e analisar a viabilidade econômica da instalação de dispositivos hidrocinéticos, além de avaliar a sustentabilidade econômica do projeto, considerando os custos de instalação, manutenção e operação em comparação com outras fontes de energia renovável e convencional.