Abstract
Ground Penetrating Radar (GPR) is a geophysical imaging technique used for the characterization of a sub surface’s electromagnetic properties, allowing for the detection of buried objects. The characterization of an object’s parameters, such as position, depth and radius, is possible by identifying the distinct hyperbolic signature of objects in GPR B-scans. This paper proposes an automated system to detect and characterize the presence of buried objects through the analysis of GPR data, using GPR and computer vision data pro cessing techniques and YOLO segmentation models. A multi-channel encoding strategy was explored when training the models. This consisted of training the models with images where complementing data processing techniques were stored in each image RGB channel, with the aim of maximizing the information. The hy perbola segmentation masks predicted by the trained neural network were related to the mathematical model of the GPR hyperbola, using constrained least squares. The results show that YOLO models trained with multi-channel encoding provide more accurate models. Parameter estimation proved accurate for the object’s position and depth, however, radius estimation proved inaccurate for objects with relatively small radii. © 2024 by SCITEPRESS– Science and Technology Publications, Lda.

Abstract
With the expansion of offshore infrastructure, the necessity for efficient Operation and Maintenance (O&M) procedures intensifies. This article introduces DADDI, a multimodal dataset obtained from a real offshore floating structure, aimed at facilitating comprehensive inspections and 3D model creation. Leveraging Unmanned Aerial Vehicles (UAVs) equipped with advanced sensors, DADDI provides synchronized data, including visual images, thermal images, point clouds, GNSS, IMU, and odometry data. The dataset, gathered during a campaign at the ATLANTIS Coastal Testbed, offers over 2500 samples of each data type, along with intrinsic and extrinsic sensor calibrations. DADDI serves as a vital resource for the development and evaluation of algorithms, models, and technologies tailored to the inspection, monitoring, and maintenance of complex maritime structures. © 2024 IEEE.

Abstract
Background: With the emergence of eHealth and mHealth, the use of mental health apps has increased significantly as an accessible and convenient approach as an adjunct to promoting well-being and mental health. There are several apps available that can assist with mental health monitoring and management, each with specific features to meet different needs. The intersection of mental health and cyber technology presents a number of critical legal and ethical issues. As mental health monitoring apps and devices become more integrated into clinical practice, cybersecurity takes on paramount importance. Objective: To address the ethical and legal aspects of health cybersecurity related to applications in mental health monitoring and management. Methods: We carried out a thematic synthesis of the best scientific evidence. Results: These tools have the potential to significantly improve access to and quality of care for users with mental health conditions, but they also raise substantial concerns about privacy and informed consent.  Cybersecurity in mental health is not only a matter of technology, but also of human rights. The protection of sensitive mental health information is critical, and legal and ethical measures to safeguard this information must be implemented in a robust and transparent manner. Conclusion: the use of information technologies and mobile devices is now part of the clinical reality and its future perspectives. It is important to mention that while these apps can be helpful for self-care and mental well-being management, they are not a substitute for the advice and support of a qualified mental health professional (psychologist or psychiatrist). As we move into the digital age, it is imperative that mental health monitoring and management apps are developed and used responsibly, ensuring the safety, dignity, and well-being of users.

Abstract
Additive manufacturing (AM), broadly known as 3D printing, is transforming how products are designed, produced, and serviced in public health. Recent advances on 3D printing in healthcare have led to lighter, stronger and safer products, reduced lead times and lower costs. However, literature refers that knowledge remains one of the greatest barriers to AM's wider adoption. So, how we leverage the potential of AM to drive innovation is a mandatory topic in science/technology curriculum.Our goal was to develop and implement an educational scenario regarding 3D printing that uses project-based learning to address these topics, strengthening the capacity of students in low secondary level and their schools to promote STEM learning with a focus on public health issues. The scenario supports 9th grade science and ICT teachers in exploring 3D printings and environments using updated scientific/technical evidence.Overall, three schools took part in the study and 202 students participated in the educational scenario. The learning experience supports youths in understanding and reaching high-level comprehension on how STEM may contribute to address these issues, contributing to evidence-based personal decision-making, and public policy. We believe it is relevant to understand if students and schools, when challenged, take a role in their community preparedness for major health problems.By implementing an educational scenario with a focus on 3D printing, and thus potentiate the use of this technology, we intend to help raise awareness on the public health theme. © 2025 Elsevier B.V., All rights reserved.

Abstract
This work was developed to improve the costing process of new products within the Product Development Department of a furniture manufacturer. It consisted of creating a parametric cost estimation model based on applying simple and multiple linear regressions, considering the existing data of the products produced and their respective costs. The proposed model considers the cost estimation of creating a product that covers the materials and operations costs. The suitability of the different independent variables was studied by applying simple and multiple linear regressions. A set of functions that return an estimate of the cost as a function of these predictor variables was obtained. The model built with the functions obtained provides the materials and operations cost estimation. The results indicated that 75% of the tests performed show an estimation error of less than 2% in the total cost of a product. Incorporating this model in a tool with the purpose of cost estimation brings the ability to predict prices faster, improving the internal process of obtaining costing and enhancing the analytical capacity of the team in the relentless pursuit of cost minimization and value creation.

Abstract
Core loss estimation in switched reluctance motor is a complex task, due to non-linear phenomena and non-sinusoidal flux density waveforms. Several methods have been developed for estimating it (e.g. empirical, and physical-mathematic models), each one with merits and limitations. This paper proposes a new method for core losses estimation based on Finite Element Method Magnetics software. The main idea is using the machine phase-current harmonics as input for estimating core losses. In addition, a comparative study is carried out, where the proposed approach is faced up to a different one, based on Fourier decomposition of the flux density waveforms in the machine sections. In order to systematically analyze and compare the applied estimation cores loss techniques, a case study of a three-phase 6/4 SRM for different simulation scenarios is introduced. The outcomes of both methods are discussed and compared, where core loss convergence is found for limited speed and load ranges.