Abstract
The limited availability and high cost of acquiring real-world image data impacts the creation of high-quality datasets, hindering the development of robust machine learning models, particularly in complex visual domains. This paper investigates the feasibility of enhancing image classification performance by incorporating balanced synthetic data into existing datasets. Three distinct machine learning tasks—image classification, instance detection, and image segmentation—were explored across diverse image domains. Synthetic images were generated to complement real-world data, and various testing scenarios were conducted, adjusting the relative weights of real and synthetic samples. The results demonstrate that balanced datasets, comprising an equitable mix of real and synthetic images, consistently yielded the highest performance metrics across all tasks. It was also observed that even a small introduction of synthetic data can improve performance over real data alone. The 50–50 split showed to optimally balance the realism of real data and the variability of synthetic data. Real data ensures that the model learns accurate representations of objects, while synthetic data enriches the training process with additional variations, reducing overfitting to specific real-world examples. The proposed approach highlights the potential of strategically integrating synthetic data to improve model accuracy and robustness, particularly in scenarios where real-world data is limited or challenging to acquire. © 2025 Elsevier B.V., All rights reserved.

Abstract
In the context of the diversity and complexity of laboratory processes, it is crucial to address the vulnerabilities associated with healthcare. Proper risk management becomes essential to ensure quality and safety in this environment. In this sense, the application of risk management tools and methodologies plays a crucial role in the identification, assessment and mitigation of potential risks present in laboratory processes performed, especially in a hospital environment. The present work addresses the theme of risk and safety management in a hospital environment, with the aim of promoting a safe environment for this community. The Healthcare Failure Mode and Effect Analysis methodology was applied to identify and mitigate the risks associated with medical equipment used in a medical genetics laboratory. The methodology included data collection, failure analysis, risk quantification, decision tree application and risk evaluation. Among the 19 failures analyzed none demonstrated a Risk Priority Number (RPN) greater than 8, suggesting that the equipment operates within acceptable risk thresholds. The results highlighted the importance of the safety of healthcare professionals and the proper functioning of equipment to ensure patient safety. The study contributed to the development of preventive and corrective actions, as well as providing future improvements and implementation of the methodology in other services of the hospital. © 2025 Elsevier B.V., All rights reserved.

Abstract
This research seeks to raise awareness about the challenges faced by people with visual impairments by immersing users in a virtual environment that simulates 18 different visual conditions. Through a series of tests, participants are tasked with performing simple activities while navigating the complexities of these impairments. The study, validated by 60 users, uses objective metrics like reaction time and accuracy to measure the impact of these conditions on task performance. An online pre- and post-test questionnaire also reveals a significant increase in empathy among participants. The results highlight the importance of direct experience in understanding the challenges of people with visual impairments and demonstrate the potential of such simulations to foster empathy and awareness. Ultimately, this application contributes to a broader understanding of visual impairments and underscores the need for universal design initiatives.

Abstract
The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations.

Abstract
Highlights What are the main findings? The pneumatic insole can monitor, stabilize and offload plantar pressure in real time. Over 91% of measurements are reliable, with up to about 42% pressure reduction. What is the implication of the main finding? Strong potential to support foot injury prevention strategies in at-risk populations.Highlights What are the main findings? The pneumatic insole can monitor, stabilize and offload plantar pressure in real time. Over 91% of measurements are reliable, with up to about 42% pressure reduction. What is the implication of the main finding? Strong potential to support foot injury prevention strategies in at-risk populations.Abstract Plantar pressure monitoring is decisive in injury prevention, especially in at-risk populations such as people with diabetic foot. In this context, innovative solutions such as pneumatic insoles can be essential in plantar pressure management. This study describes the development of a variable pressure system that promotes the monitoring, stabilization, and offloading of plantar pressure through a pneumatic insole. This research was also intended to evaluate its ability to redistribute plantar pressure, reduce peak pressure in both static and dynamic conditions, and validate its pressure measurements by comparing the results with those obtained from a pedar (R) insole. Tests were carried out under both static and dynamic conditions, before and after the pressure stabilization process by air cells and the subsequent pressure offloading. During the validation process, methods were used to evaluate the agreement between measurements obtained by the two systems. The results of the static test showed that pressure stabilization reduced pressure on the heel by 32.43%, distributing it to the metatarsals and toes. After heel pressure offloading, the reduction reached 42.72%. In the dynamic test, despite natural dispersion of the measurements, a trend to reduce the peak pressure in the heel, metatarsals, and toes was observed. Agreement analysis recorded 96.32% in the static test and 94.02% in the dynamic test. The pneumatic insole proved effective in redistributing and reducing plantar pressure, with more evident effects in the static test. Its agreement with the pedar (R) system reinforces its reliability as a tool for measuring and managing plantar pressure, representing a promising solution for preventing plantar lesions.