JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH

Modeling and Simulation of a Bacteriological Incubator

Lucas Câncio de Jesus Cunha — 2026-07-31

Bacteriological incubator consists of equipment that provides an optimized environment for the development of bacteria, frequently used for the development and testing of antibiotics and antiseptics. This work aims to perform white box modeling and simulation of a bacteriological incubator with an operating range of up to 50 °C, presenting a comparison of the P (Proportional), PI (Proportional-Integral) and PD (Proportional-Derivative) compensators in the process loop, enunciating the advantages and disadvantages. Furthermore, the simulation was developed in the MATLAB/Simulink environment, using as reference the commercial bacteriological incubator BF115, manufactured by BINDER. As a result, for the heating process, the PI compensator meets the design requirements and it was verified in computer simulation, while the others, even for cooling, do not meet these requirements and they are not implementable.

Performance Analysis of an LCL Filter with Active Damping and Resonant Control for Grid-Connected Inverter

Társila de Jesus Santos — 2026-07-31

The growth of Distributed Generation (DG) from renewable energy sources significantly impacts the operation of electrical power systems. To enable the efficient integration of these sources into the electrical grid, inverters play a fundamental role by converting direct current (DC) into alternating current (AC). This study aims to present a simplified design project, implemented and tested through simulations in the MATLAB Simulink environment. The goal is to make resonant control techniques and active damping through capacitor current feedback more accessible and easier to understand, as well as to evaluate and demonstrate the effectiveness of the proposed methods, enabling their practical application. The results obtained showed that the control techniques employed are essential to ensure stability and power quality in inverters with LCL filters. The methodology was presented in a clear and objective manner, facilitating understanding. The simulations highlighted the reduction of the resonance peak, the effective control of the current injected into the grid, and low THD values, confirming the feasibility of applying these techniques in renewable energy systems connected to the electrical grid.

Neurobypass 1.0: A Mechanical Approach on 3D-Printed Neurocontrolled Hand Orthosis for Subacute Post-Stroke Rehabilitation

David Uzêda — 2026-07-31

Stroke is one of the leading causes of motor disability worldwide, directly impairing limb functionality and compromising individuals’ independence in daily activities. Stroke‐induced hand paresis requires assistive devices whose kinematics, compliance, and morphology faithfully emulate healthy biomechanics. Neurobypass 1.0, under SENAI CIMATEC’s ReaPTA 5.0 programme, delivers a patient-personalized hand orthosis neuro-controlled by EEG that is engineered around three mechanical pillars: biomimicry, human-like motion and additive manufacturing. The technology integrates electroencephalography (EEG) to command robotic elements via recognition of neural motor-intention patterns, additive manufacturing with flexible and rigid materials (TPU/PLA) and an immersive virtual-reality environment, delivering neurofeedback that stimulates neuroplasticity. By fusing brain-computer-interface (BCI), multi-material 3D printing, modular biomimetic geometry and minimum jerk kinematics into a single, low-cost platform tht amplifies the therapeutic impact alongside conventional therapy, providing clinicians with an evidence-driven, easily serviceable device to accelerate upper-limb functional recovery in post-stroke patients.

Personalized Learning Model for University-Industry Integration Through PBL Problem-Solving

Juliana de Santana Silva — 2026-07-31

Modern educational models recommend Problem-Based Learning (PBL) and Adaptive Learning (AL) to increase the alignment of education and work organization. PBL is a technique that focuses on learning through problem-solving. However, understanding how and why PBL works is a current challenge. To advance this issue, systems that personalize learning processes are recommended. The present study developed an AL approach for PBL to align education with industry through problem-solving. The proposed approach is based on ontologies, complex multilayer networks, Bayesian networks, a recommendation system, and multi-agent architecture. The proposed tool consists of descriptive, diagnostic, predictive, and prescriptive models. This tool is available on eduCAPES with guidelines for implementing these models. Reduced PBL planning effort, increased collaboration in the development of innovation projects, enhanced educational quality, and the availability of data for Artificial Intelligence (AI) training are some benefits of this tool. To evaluate the proposed approach, nine graduate students completed the User Experience Questionnaire. The attractiveness, hedonic quality, and pragmatic quality of the models were positively perceived. Therefore, the results showed that students have a positive perception of the model's functions. The present study was limited to experiments with a small sample of students. Evaluation of the perceptions of university, educational administrators, the graduate program, the educational institution, industry, and a larger sample of students is suggested for future studies. Currently, the model is implemented with a set of existing tools. Therefore, studies to develop a platform with a user-friendly interface that integrates the proposed models are recommended.

Development of a Permanent Bicycle Chain Maintenance and Cleaning Device

Caio Gomes — 2026-08-03

This article aims to develop an innovative solution for the dirt accumulation challenges faced by long-distance cyclists in maintaining their bicycle chains clean and functional for longer periods. The methodology employed involved a detailed analysis of existing products on the market, identifying their strengths and weaknesses. This comparative analysis served as the basis for the development of an optimized product capable of withstanding adverse weather conditions, ensuring continuous lubrication, and preventing the accumulation of dirt. The results achieved demonstrate the feasibility of a solution that extends the lifespan of the chain, reduces the need for maintenance, and significantly improves the performance and safety of cyclists, allowing them to focus exclusively on their cycling experience.

Advancing Circularity in Additive Manufacturing: Life Cycle Assessment of Post-Consumer PA12 Recycling

Nicole Príncipe C. da S. Ribeiro — 2026-08-03

Additive Manufacturing (AM) has emerged as a transformative technology in industrial production, enabling the fabrication of complex geometries with reduced material waste compared to conventional methods. Among its techniques, Powder Bed Fusion (PBF) is widely applied to polymeric materials such as polyamide 12 (PA12), producing durable components that eventually reach end-of-life. The growing volume of discarded AM parts highlights the need for sustainable end-of-life strategies, with mechanical recycling offering a promising pathway to close material loops within a circular economy. This study evaluates the environmental performance of post-consumer PA12 recycling from prototypes and parts manufactured via PBF using the HP Multi Jet Fusion 5210 printer. The assessment followed ISO 14040 and 14044 guidelines, adopting a cradle-to-gate boundary and a functional unit of 1 kg of recycled PA12 pellets (PA12r). Data were based on primary measurements at SENAI/CIMATEC’s facilities, complemented by secondary datasets from Ecoinvent 3.10.1. The product system comprised five stages: Cascalheira Milling, Cimatec Park Milling, Manual Washing, Drying, and Extrusion/Pelletizing. Life Cycle Impact Assessment (LCIA) results, focused on Global Warming Potential (GWP100, IPCC 2021), showed PA12r achieved a 10% carbon footprint reduction compared to virgin PA12. Drying was the most impactful stage in total emissions, while Extrusion/Pelletizing had the highest hourly rates, underscoring their critical roles in optimization. Electricity consumption was the dominant contributor, influenced by equipment demand and Brazil’s energy mix. Findings show the technical and environmental potential of scaling PA12 recycling for additive and
conventional manufacturing, while addressing a literature gap and supporting recycling integration in AM supply chains to boost circularity and reduce impacts.

A Proposal for Renewable Energy Viability and Environmental Conservation in the Caatinga: Challenges and Opportunities

Toni Alex Reis Borges — 2026-08-04

The need to preserve the Caatinga, characterized by a semiarid climate, high temperatures and low humidity with long periods of drought, while considering methods that could utilize these characteristics to minimize their adverse effects, poses a challenge. Despite this need, climate change has sparked significant global concern through the reduction of CO2 emissions. Given this scenario, clean energy generation presents an important alternative to overcome climate challenges. This study seeks to bridge these two needs by sharing resources from irrigation systems developed under the Integrated Development Region strategy. To this end, it seeks to integrate the use of renewable energy supported by a clustering model, grouping different municipalities to ensure the implementation of irrigation infrastructure with democratic participation. The research locus is the state of Ceará, the only state 100% located in this Caatinga region. The solar energy potential of the Northeast region represents a major economic investment in the short term, but with a reduction in the medium and long term. Furthermore, its technical feasibility requires local energy infrastructure and, from a socio-environmental perspective, can foster cooperative and associative work through the sharing economy due to the subdivision of solar panels. Based on this scenario, the results suggest that the use of optimal location points can contribute to the development of more resilient and sustainable energy solutions, benefiting both the environmental preservation of the Caatinga region and the generation of clean energy, the latter supported by the region's solar energy potential.

Multiagent System for Discrete Manufacturing with Integration into the MES/SCADA System and Production Decision Support

Kevin Luís dos Santos — 2026-08-04

Discrete manufacturing faces significant challenges related to high variability, mass customization, and the demand for rapid responses to operational failures, which increase costs and compromise process efficiency. Industry 4.0 emerges as a response to this scenario by promoting digitalization and intelligent system integration, enabling real-time monitoring and dynamic adaptation of production. The integration between the Manufacturing Execution System (MES) and Multi-Agent Systems (MAS) becomes essential to optimize processes, enable greater decision-making autonomy, and increase the resilience of the production system. This work proposes a multi-agent approach to optimize production in discrete manufacturing environments, integrating with MES/SCADA systems and establishing the foundations for a Digital Twin with Artificial Intelligence (AI) capabilities. The methodology was applied at the Advanced Manufacturing Plant (PMA) of SENAI CIMATEC, a demonstrative Industry 4.0 environment. The initial steps involve defining the agents, modeling the system, and integrating with MES, using a Service-Oriented Architecture (SOA) to expose functionalities as microservices. The results include the mapping and classification of PMA components, as well as the proposal of a preliminary agent structure, modeled in UML and divided into two groups: Device Agents, responsible for field data collection, and Management and Analysis Agents. It is expected that the Analyzer Agent will play a key role in detecting deviations or potential failures in the process by employing AI to predict faults and suggest rescheduling. The proposed solution offers a conceptual basis that integrates traditional technologies and intelligent agents to build an AI-enabled Digital Twin, capable of supporting dynamic rescheduling and adaptive control, enhancing responsiveness to changes and unforeseen events.

Optimization of Code for Scalability of Deep Reinforcement Learning Agents in Slow and Stochastic Industrial Process Patterns

Guilherme Peniche Cordeiro — 2026-08-04

This work investigates the impact of code optimization techniques on the scalability and training time of deep reinforcement learning (DRL) agents applied to stochastic and slow-dynamic industrial process control environments. DRL combines deep learning and reinforcement learning to address high-dimensional decision-making problems, but it often demands significant computational resources and time, especially in complex industrial scenarios. To address these challenges, this research explores the use of vectorization, parallelism, and hyperparameter optimization via Optuna to improve performance and training efficiency. Experiments were conducted using the “TempControl-v0” environment from the PIDGym library, simulating thermal inertia processes akin to industrial furnaces. Key metrics analyzed include total training time, average episode reward, and CPU/GPU utilization. The results demonstrate that strategic code optimization can significantly enhance the performance and scalability of DRL agents, making their application more feasible in real-world industrial contexts.

Energy Justice and Microgrids in Vulnerable Urban Areas: A Sustainable Approach

Celso Barreto da Silva — 2026-08-04

This article explores the critical intersection between energy justice and the transformative role of microgrids in socially vulnerable urban areas. The research addresses inequalities in energy access that affect these communities, highlighting how the implementation of microgrids can promote equity, resilience, and sustainable development. The social, economic, and environmental benefits of integrating microgrids were analyzed, including benefits to communities, fostering the local economy, and reducing carbon emissions. The results achieved demonstrate that microgrids not only ensure a more reliable and accessible energy supply but also empower communities, allowing them greater control over their energy resources. The analysis of global success cases illustrates the viability and positive impact of these solutions, despite technical, financial, and regulatory challenges. We conclude that microgrids are an ally in building a more just and equitable energy future, with significant implications for public policies and future research aimed at overcoming energy poverty and promoting urban sustainability.

Scene Classification in Rural Environments for Autonomous Driving Using U-Net and ResNet

Victor M. A. do Nascimento — 2026-08-04

This paper addresses the semantic segmentation of road images, a critical task for autonomous vehicle navigation, particularly in non-urban environments that present significant challenges. While much research focuses on well-maintained roads in developed countries, this study confronts the complexities of real-world conditions, such as those prevalent in developing nations, which feature vast networks of unpaved and poorly maintained roads. The core of our methodology is a neural network architecture that synergistically combines the encoder-decoder structure of U-Net with the feature extraction power of a ResNet backbone. The primary objective is the precise classification of each image pixel into one of four essential categories for navigation: background, asphalt, paved, and unpaved road. The model's training regimen involved exploring different ResNet versions (ResNet18, ResNet34, and ResNet50) as the encoder backbone to assess the impact of network depth. A key aspect of our approach was a progressive training strategy, where model versions were trained on images of varying resolutions. The results demonstrated a significant and somewhat counter-intuitive finding: training the ResNet34-U-Net model with images at half the original resolution yielded the best overall performance, achieving the highest Dice and IoU scores. This suggests that reducing image resolution acts as an effective form of regularization, compelling the model to learn more general and robust features by ignoring minor, irrelevant details. This outcome not only enhances the model's generalization capabilities for diverse and imperfect road conditions but also carries a substantial practical advantage by reducing the computational cost of training and inference, a crucial factor for deployment on resource-constrained embedded systems in autonomous vehicles.

Comparative Analysis of Functional Verification Strategies for RISC-V with Potential Use in CV-QKD Systems

Gustavo Santiago Sousa — 2026-08-04

With the advancement of quantum computing, traditional cryptographic algorithms have become vulnerable, demanding solutions based on physical principles, such as continuous-variable quantum key distribution (CV-QKD). One of the main challenges for the practical adoption of CV-QKD lies in the digital signal processing (DSP) and post-processing stage, which imposes strict requirements for performance, reliability, and robustness against noise and faults. In this context, architectures based on the open fifth-generation Reduced Instruction Set Computing (RISC-V) standard have proven promising for building application-specific instruction set processor (ASIP) and specialized system-on-chip (SoCs), due to their flexibility, customization capabilities, and growing support ecosystem. However, ensuring the functional reliability of such systems requires the application of rigorous verification processes. This paper presents a comparative study of four complementary functional verification approaches: modular block-based verification, instruction-level transaction-based verification, black-box verification, and cycle-by-cycle comparison. Each approach was evaluated in terms of coverage, implementation complexity, and fault visibility. The study also highlights how these methods complement each other when applied in layered verification environments. The results show that no single approach can efficiently meet all requirements. High-level strategies are better suited for integration testing and broad functional validation, while low-level techniques are essential for localized debugging and precise timing analysis. This reinforces that verification success relies more on strategic integration than on a single optimal method. The findings indicate that adopting hybrid, layered solutions provides better scalability, supports regression testing, and improves both coverage and alignment with the security and performance goals required by CV-QKD systems.

Christmas Tree Market Analysis for the Upstream Oil and Gas Sector

Felipe Pinto Oliveira — 2026-08-04

The oil and gas sector is central to the global economy, providing both energy inputs and industrial raw materials. Within the upstream segment, a critical piece of equipment stands out: the "Christmas tree," a robust assembly of valves and fittings installed at the wellhead of producing wells to regulate hydrocarbon flow. This study maps the commercial dynamics and supply strategies of major global operators (Saudi Aramco, CNPC, ExxonMobil, and Chevron) in relation to their Christmas tree suppliers. The methodology consisted of a broad literature review, including the analysis of corporate and technical reports from 2003 to 2025, as well as publications from oil and gas business platforms. The findings reveal a heterogeneous and strategically structured network of commercial interactions. Saudi Aramco maintains strong, long-term relationships with suppliers such as Dril-Quip and TechnipFMC, which develop surface and subsea Christmas tree models tailored to the specific requirements of its projects. In a distinct approach, CNPC diversifies its supplier base by prioritizing domestic manufacturers such as BOMCO, Sanjack Petro, and Cepai, integrating solutions that include horizontal subsea equipment. Conversely, ExxonMobil and Chevron converge on a multilateral approach, establishing long-term contracts with the three largest global suppliers: TechnipFMC, SLB (OneSubsea), and Baker Hughes. These partnerships are characterized by the adoption of cutting-edge technologies, including Aptara™, Subsea 2.0™, and monobore configurations, all aimed at maximizing efficiency and operational safety. It is concluded that major upstream companies maintain a diversified supplier portfolio as a strategic cornerstone. The analysis demonstrates that supplier diversity reflects corporate policies focused on innovation, energy security, and, in some cases, the strengthening of local supply chains, thereby ensuring resilience and competitive advantage in the global landscape.

Biorefinery of Cocoa Waste: A Systematic Review and Bibliometric Analysis

Yan Valdez Santos Rodrigues — 2026-08-04

The growing demand for sustainable alternatives to produce biofuels and bioproducts has driven the development of biorefineries based on agro-industrial residues. Cocoa (Theobroma cacao L.), an important agricultural commodity, generates approximately 70–80% of residues during its processing, presenting significant potential for use in integrated conversion processes. This study presents a systematic literature review combined with a bibliometric analysis to answer the question: Do cocoa residue biorefineries exist? The literature search was conducted using the Scopus and Web of Science databases, resulting in the selection of 32 articles. The main publication trends, countries, journals, authors, institutions, and associated keywords were analyzed. The results show that scientific production on cocoa residue biorefineries is emerging, concentrated in cocoa producing countries in Latin America and Asia, and strongly related to bioenergy routes, bioactive compounds, and the circular economy. This analysis provides both quantitative and qualitative insights to support the development of biorefinery proposals and the identification of research gaps.

Exploring Prosopis juliflora as a Sustainable Biomass Source for Bioethanol: A Review of Processing Methods and Production Potential

Geise Camila de Araujo Ribeiro — 2026-08-04

As the 20th century progressed, the accelerated growth of industry and the consistent increase in population size in major urban areas contributed to an escalating release of greenhouse gases, including carbon dioxide (CO2). Fuels derived from renewable materials, such as plant biomass, offer an alternative to traditional fuels, providing a more environmentally friendly energy source. A highly relevant alternative would be to align the development of sustainable technologies with the bioprocessing of biomass waste from non-food sources. The tree Prosopis juliflora (P. juliflora) is a promising candidate due to its accessibility and energetic properties. The success of P. juliflora biofuel hinges on pretreatment techniques that maximize enzymatic accessibility to cellulose. This study systematically reviews the extant research on P. juliflora as bioethanol feedstock, with a focus on identifying which plant parts are used, the treatments applied to these parts, and the methods employed for biomass breakdown. The research methodology entailed a bibliometric analysis, which was conducted using the Web of Science database between July 25, 2025, and August 5, 2025. The Vosviewer® software was utilized for the analysis of keyword co-occurrence and author co-citation. The search yielded 29 publications and 32 keywords, with a minimum of three occurrences in the analyzed articles. Of these, 20 publications met the study's scope. The findings of the study demonstrate that chemical pretreatment provides the optimal balance between delignification and cellulose preservation. Acid degradation is still preferred over enzymatic hydrolysis, as it is the approach used in most of the studies that were analyzed. While there are studies that have applied P. juliflora in the production of bioethanol using these strategies, the development of more sustainable research and methodologies is still necessary to obtain biofuel in an effective and ecological way.

Potential of Agave sisalana in the Brazilian Semi-Arid Region for Ethanol and Biogas Production

Tiago Nunes da Cruz Santos — 2026-08-04

This study aims to conduct a bibliographic assessment of the potential of agave as a raw material for biofuel production in the Brazilian semi-arid region, considering agronomic, technological, and environmental aspects. Agave, a plant native to arid and semi-arid regions, stands out for its high biomass productivity and ability to adapt to conditions of low water availability. These characteristics make it a promising alternative for the sustainable production of biofuels, especially in areas less suitable for conventional agriculture. Unlike traditional crops such as sugarcane, agave cultivation does not compete directly with food production, which contributes to food security. Studies show that agave species can achieve ethanol yields of between 7,400 and 10,000 liters per hectare, which is competitive with or higher than the yields obtained from sugarcane in dry regions. In addition to ethanol, the bagasse and residual juice from the leaves can be used to generate biogas and biomethane, expanding energy utilization routes and assisting in the management of agro-industrial waste. Thus, agave has strategic potential to strengthen the renewable energy matrix, generate socioeconomic and environmental benefits, and contribute to climate change mitigation goals.

UAV-Driven Maritime Object Detection and Classification: Literature Review of AI Models, Data, and Performance

Laurinne Lorenna Cristina Toscano Lira de Oliveira — 2026-08-04

This literature review explores the integration of UAVs and AI in maritime surveillance, analyzing 59 peer-reviewed studies from 2020–2025. It highlights trends in object detection and classification, common methodologies, model architecture (notably YOLO), and evaluation metrics like mAP. Detection targets include boats, ships, and swimmers, with varying research focus. The review identifies technological approaches, research gaps, and growing interest in UAV-based maritime monitoring.

Bibliometric Mapping of Human-Machine Interfaces in Autonomous and Intelligent Systems

Erika Dias Nascimento Marques — 2026-08-04

The growing integration of autonomous systems in industry has reinforced the demand for efficient Human-Machine Interfaces (HMIs) to ensure safe and adaptive interactions. This study presents a bibliometric analysis of 99 publications (2015–2025) on supervisory interfaces, using Scopus data and tools such as VOSviewer and Bibliometrix. The results revealed six main clusters, centered on terms like human-machine interface, process control, machine learning, and decision-making. The analysis highlights the leadership of China, the United States, and India, while showing the absence of Brazilian contributions. A marked rise in publications occurred after the COVID-19 pandemic, although gaps persist in applied studies involving biosystems.

SARA: An LLM-Based Approach for Systematic Literature Review with a Use Case on Classical–Quantum Integration for Secure Communication

Naiara Santos — 2026-08-04

This work presents SARA (Semi-Automated Review and Analysis System), an approach that integrates Large Language Models (LLMs), topic modeling, and semantic search to support Systematic Literature Reviews (SLRs). As a use case, SARA was applied to the domain of integration between classical networks and secure quantum communications, covering Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC). The system performed deduplication, pre-selection, categorization, and ranking of articles, prioritizing publications with higher potential for scientific contribution. The results indicate a predominance of studies on QKD, followed by PQC and hybrid approaches, with emphasis on telecommunications, IoT, and critical systems. SARA proved effective in identifying publications aligned with the researcher’s queries as well as conceptually related studies, demonstrating its potential to optimize SLR processes in emerging technological areas.