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
Stroke is a leading cause of long-term disability worldwide, with survivors often facing significant challenges in regaining upper-limb functionality. In response, robotic rehabilitation systems have emerged as promising tools to enhance post-stroke recovery by delivering precise, adaptable, and patient-specific therapy. This paper presents a review of robotic interfaces developed specifically for upper-limb rehabilitation. It analyses existing exoskeleton- and end-effector-based systems, with respect to three core design pillars: assistance types, control philosophies, and actuation methods. The review highlights that most solutions favor electrically actuated exoskeletons, which use impedance- or electromyography-driven control, with active assistance being the predominant rehabilitation mode. Resistance-providing systems remain underutilized. Furthermore, no hybrid approaches featuring the combination of robotic manipulators with actuated interfaces were found. This paper also identifies a recent trend towards lightweight, modular, and portable solutions and discusses the challenges in bridging research prototypes with clinical adoption. By focusing exclusively on upper-limb applications, this work provides a targeted reference for researchers and engineers developing next-generation rehabilitation technologies.

Abstract
Radio-Frequency IDentification (RFID) technologies automate the identification of objects and persons, having several applications in retail, manufacturing, and intralogistics sectors. Several works explore the application of RFID systems in robotics and intralogistics, focusing on locating robots, tags, and inventory management. This paper addresses the challenge of intralogistics cargo trolleys communicating their characteristics to an autonomous mobile robot through an RFID system. The robot must know the trolley's relative pose to avoid collisions with the surroundings. As a result, the passive tag on the cargo communicates information to the robot, including the base footprint of the trolley. The proposed RFID system includes the development of a controller board to interact with the frontend integrated circuit of an external antenna onboard the industrial mobile robot. Experimental results assess the system's readability distance in two distinct environments and with two different antenna modules. All the code and documentation are available in a public repository.

Abstract
As digital design methodologies and tools are evolving to higher abstraction levels, teaching the low-level concepts of digital electronic system design is becoming increasingly challenging. The raise of the design abstraction level and, more recently, the ability of AI-assisted automated design is pushing the interest of students away from the lower-level details of the digital world. Nevertheless, digital electronic systems are (still) made of transistors, gates and flip-flops, and people do need to keep this basic knowledge to be able to build efficient circuits, understand them and develop the essential electronic design automation tools. For learning these subjects, hands-on experimentation, and learning by doing, is proven to be an effective tool, and when students finally see and feel the results of their designs, motivation raises rapidly. This paper presents the technical aspects of a platform created in the DECEL project to support an FPGA-based remote laboratory based on a commercial single-board computer that can be located somewhere in the Internet. This computer runs a Linux operating system and is based on an AMD/XILINX device that integrates in the same chip an ARM Cortex A9 CPU and a region of FPGA programmable logic. The user develops a digital circuit using standard hardware-description languages (Verilog or VHDL) and runs the implementation tools for the target FPGA using a very simple web interface running in a remote server.

Abstract
This demo shows an infrastructure that allows for easy implementation of real remote labs. In this infrastructure, several nodes are remotely interconnected by publishing/subscribing MQTT messages. There are physical nodes capable of connecting to real circuits and/or sensors/actuators, and virtual nodes that implement simulated versions of circuits that interact remotely with signals from other nodes. The latencies that occur are low enough for groups of students located in different physical locations to benefit from a near real-time experience in interacting with the circuits thus implemented.