Abstract
Background: One of the most common consequences in individuals with diabetes is the diabetic foot, which can cause foot ulcers and even lead to limb amputation. Since an increase of the temperature in the plantar region is directly correlated with an increased risk of ulceration, infrared thermography (IRT) has been used in multiple studies as an automatic tool for detecting problems in diabetic foot. Artificial intelligence-based computer-aided diagnosis systems are being more frequently used to improve decision-making and minimize errors. These technologies are designed to increase examination accuracy, consistency in image interpretation, prognosis evaluation support, and examination accuracy. They also have the ability to offer insightful information and help medical professionals to manage diabetic foot issues successfully.Methods: In this work, 37 papers that used thermography and artificial intelligence (AI) to identify diabetic foot complications and/or predict the risk of developing diabetic foot are analyzed.Results: The results demonstrate the potential of IRT imaging implementation with AI for the identification and prediction of diabetic foot complications.Conclusions: The combination of IRT and AI shows significant potential for diabetic foot assessment; however, the great majority of these studies show that the research is confined to classification of foot thermograms using pre-prepared data sets. In particular, there is limited research on segmentation methods and constraints in the use of deep learning due to the lack of large and diverse datasets.

Abstract
Background. Sample size estimation in Structural Equation Modeling (SEM) remains a debated methodological issue. Although minimum sample size rules are widely used, they often lack integration of theoretical, statistical, and practical considerations, particularly in complex research designs. Objectives. This study aims to provide a synthesis and comparative analysis of different approaches to sample size estimation in SEM, integrating theoretical foundations, statistical criteria, methodological limitations, and applied research contexts. Materials and Methods. A multi-method approach was employed, consisting of three components: a narrative review of sample size estimation methods, consideration of simulated scenarios reflecting realistic research conditions, and a comparative evaluation of method performance across these scenarios. Results. Findings show that rule-of-thumb approaches are useful for preliminary estimates but may misrepresent required sample sizes in complex models. Power analysis provides more precise and rigorous estimates, particularly for confirmatory research. Monte Carlo simulations offer robust guidance for complex, longitudinal, and multigroup models. Estimator-based recommendations highlight the importance of aligning sample size with data characteristics and estimation techniques. Model complexity emerged as a key determinant, with advanced SEM models often requiring 400–700+ participants. Conclusions. A systematic and context-sensitive approach to sample size estimation is essential for ensuring robustness and methodological rigor in SEM studies across disciplines such as management and education.

Abstract
As collaborative robots increasingly redefine industrial automation, understanding the factors that drive their adoption is essential to operations management. This study examines the main drivers of collaborative robot adoption in the Brazilian manufacturing sector by combining theory-driven framing with a machine learning classification approach. It was developed a Random Forest classifier to identify the strongest predictors of cobot adoption and to rank their relative importance. Data were collected from a sample of respondents-primarily managers and chief executive officers-representing 300 industrial companies. Grounded in the Technology-Organization-Environment (TOE) framework and complemented by Diffusion of Innovations (DoI) and Institutional (INT) perspectives, the analysis shows that technological advantages, namely space efficiency, cost reduction, and ease of integration, are critical drivers of adoption. Organizational factors, including proactive managerial involvement and alignment with an innovation-oriented culture, significantly increase the likelihood of collaborative robot uptake. The model demonstrated robust predictive performance and produced interpretable variable importance scores that confirm the relative influence of technological and managerial factors. These findings provide a structured lens for understanding and guiding managerial decision-making on cobot adoption and translate into practical recommendations for managers.

Abstract
Ensuring the security of Industrial Control Systems (ICS) is increasingly critical due to increasing connectivity and cyber threats. Traditional security measures often fail to detect evolving attacks, necessitating more effective solutions. This paper evaluates machine learning (ML) methods for ICS cybersecurity, using the ICS-Flow dataset and Optuna for hyperparameter tuning. The selected models, namely Random Forest (RF), AdaBoost, XGBoost, Deep Neural Networks, Artificial Neural Networks, ExtraTrees (ET), and Logistic Regression, are assessed using macro-averaged F1-score to handle class imbalance. Experimental results demonstrate that ensemble-based methods (RF, XGBoost, and ET) offer the highest overall detection performance, particularly in identifying commonly occurring attack types. However, minority classes, such as IP-Scan, remain difficult to detect accurately, indicating that hyperparameter tuning alone is insufficient to fully deal with imbalanced ICS data. These findings highlight the importance of complementary measures, such as focused feature selection, to enhance classification capabilities and protect industrial networks against a wider array of threats.

Abstract
Glaucoma is one of the leading causes of irreversible blindness worldwide, affecting millions of people, often silently and progressively. Early diagnosis is crucial to slow its progression, but it remains challenging due to the need for manual analysis of large volumes of retinal images by trained specialists. In this context, automatic detection systems based on deep learning offer a promising opportunity to facilitate and accelerate the diagnostic process, providing scalability and high accuracy. This work presents the development of an automatic method for optic disc and optic cup segmentation in retinal fundus photographs, aiming to support early glaucoma detection. The proposed methodology is based on convolutional neural networks (CNNs), specifically an enhanced U-Net architecture with a ResNet50 backbone, incorporating attention mechanisms and data augmentation strategies to improve segmentation accuracy. The model was trained and validated using the REFUGE dataset, which contains high-quality fundus images with manual annotations of the disc and cup regions. Experimental results demonstrate that the developed model achieved an average Dice coefficient of 0.937 for optic disc segmentation and 0.828 for optic cup segmentation. Analysis of the cup-to-disc ratio (CDR) yielded mean values of VCDR = 0.497 ± 0.059, ACDR = 0.252 ± 0.060, and mean CDR = 0.375 ± 0.058, with 55.0% of cases classified as low risk, 43.3% as moderate risk, and 1.7% as high risk for glaucoma. These results highlight the potential of the proposed method as an assistive tool for automated glaucoma screening.

Abstract
Product design is a complex and iterative process that requires the balance of multiple constraints, such as material selection, manufacturability, regulatory compliance, and structural integrity, among others. Traditional design workflows follow a human-driven approach, limiting efficiency, adaptability, and the ability to quickly respond to evolving limitations. This paper introduces an agentic approach to product design, leveraging multi-agent systems to distribute and automate design tasks dynamically. To demonstrate this methodology, a hypothetical enclosure design is used as a guiding example, demonstrating how agents interact to generate product specifications, select materials, validate structural properties, assess manufacturability, and perform other relevant tasks throughout the design process. To implement this framework, CrewAI is utilized as an agent coordination system that enables the structured definition of roles and execution of tasks for autonomous agents. In the final section, a case study is presented, focusing on the design of a parallelepiped enclosure, applying the proposed framework in a simulated environment. Our findings highlight the advantages of agent-based collaboration in product design, showcasing its potential to optimize workflows, reduce development time, and improve adaptability to changing requirements.