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.

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.

Abstract
The implementation of lean principles in product development (PD) activities has been receiving increased attention lately. However, it is not clear how the application of these principles to PD activities enhances their effectiveness. Moreover, the implementation of lean principles is more difficult to achieve in PD activities than in the shop-floor context. The objective of this paper is to develop and implement a framework applying lean principles to the PD process. To that end, an action research project was conducted in the R&D department of an industrial company. This article presents and describes a six-step framework, its challenges, and main results. The implementation of the framework led to gains in the efficiency of the product development process through a 20% decrease in waste. Improvement measures such as standardisation, clear identification of roles, prioritisation of activities and improved efficiency of meetings were the main drivers for the gains in efficiency. Overall, three main contributions should be highlighted: the role a knowledgeable lean project leader can play; employee training focused on the implementation of lean-based product development activities; and team building and communication.

Abstract
This article proposes the Artificial Intelligence Models Switching Mechanism (AIMSM), a novel approach to optimize system resource utilization by allowing systems to switch AI models during runtime in dynamic environments. Many real-world applications utilize multiple data sources and various AI models for different purposes. In many of those applications, every AI model doesn’t have to operate all the time. The AIMSM strategically allows the system to activate and deactivate these models, focusing on system resource optimization. The switching of each AI model can be based on any information, such as context or previous results. In the case study of an autonomous mobile robot performing computer vision tasks, the AIMSM helps the system to achieve a significant increment in performance, with a 50% average increase in frames per second (FPS) rate, for this specific case study, assuming that no erroneous switching occurred. Experimental results have demonstrated that the AIMSM can improve system resource utilization efficiency when properly implemented, optimize overall resource consumption, and enhance system performance. The AIMSM presented itself as a better alternative to permanently loading all the models simultaneously, improving the adaptability and functionality of the systems. It is expected that using the AIMSM will yield a performance improvement that is particularly relevant to systems with multiple AI models of a complex nature, where such models do not need to be all continuously executed or systems that will benefit from lower resource usage. Code is available at https://github.com/BrunoGeorgevich/AIMSM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract