The discussion about first-year engineering students’ difficulties in mathematics is continuous in the fields of engineering, mathematics and higher education. The present study aimed to examine the initial barriers academic math teachers need to have in mind if they want to improve students’ performance in engineering math courses through appropriate teaching practices in order to face their initial interindividual differences. During the first phase of the study, we examined first year engineering students’ initial beliefs about the nature of mathematics, their self-efficacy beliefs about mathematics and their basic mathematical knowledge. The math school grade was used for their previous mathematical performance. Results indicated the predominant role of the previous mathematical knowledge and the important role of the formalistic disposition toward mathematics. The lack of experience of using mathematics for problem-solving situations within an engineering framework prevented students from recognizing and appreciating the value of mathematical courses during the engineering studies. The second phase of the study examined, through an interview with a group of students, their perceptions of the teaching practices which were introduced after their teacher attended a training program. The discussion concentrates on how academics can use teaching processes for equity and not equality in order to motivate their students.

Background: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imaging, particularly in scenarios involving small temperature gradients, like those in the human body. Methods: In this study, we employed 3D convolutional neural networks (CNNs) to predict internal temperature fields. The network’s performance was evaluated under both ideal and non-ideal conditions, incorporating noise and background temperature variations. A physics-informed loss function embedding the heat equation was used in conjunction with statistical uncertainty during training to simulate realistic scenarios. Results: The CNN achieved high accuracy for small phantoms (e.g., 10 cm in diameter). However, under non-ideal conditions, the network’s predictive capacity diminished in larger domains, particularly in regions distant from the surface. The introduction of physical constraints in the training processes improved the model’s robustness in noisy environments, enabling accurate reconstruction of hot-spots in deeper regions where traditional CNNs struggled. Conclusions: Combining deep learning with physical constraints provides a robust framework for non-invasive thermal imaging and other applications requiring high-precision temperature field reconstruction, particularly under non-ideal conditions.

The objective of this article is to present a concept for single-frequency Global Navigation Satellite System (GNSS) positioning local ionospheric mitigation over a certain area. This concept is based on input parameters driving the NeQuick-G algorithm (the ionospheric single-frequency GNSS correction algorithm adopted by Galileo GNSS system), estimated on a local as opposed to a global scale, from ionospheric characteristics measured by a digital ionosonde and a collocated dual-frequency Total Electron Content (TEC) monitor. This approach facilitates the local adjustment of Committee Consultative for Ionospheric Radiowave propagation (CCIR) files and the Az ionization level, which control the ionospheric electron density profile in NeQuick-G, therefore enabling better estimation of positioning errors under quiet geomagnetic conditions. This novel concept for local ionospheric positioning error mitigation may be adopted at any location where ionospheric characteristics foF2 and M(3000)F2 can be measured, as a means to enhance the accuracy of single-frequency positioning applications based on the NeQuick-G algorithm.

The discussion about first-year students’ difficulties in mathematics is continuous in the fields of economics, business, administration and engineering. The present study examined the impact of self-assessment online quizzes on students’ mathematical performance and the development of their persistence to work on a mathematical task. The participants’ students attended an obligatory introductory course on Mathematics as part of a program in Business Administration. Their performance was compared with the respective students’ performance the previous academic year, who did not use the online quizzes. Results indicated that students with medium and high initial performance tended to have a higher positive impact on their performance through the use of the self-assessment online quizzes than students with low performance. The discussion concentrates on how academic math teachers can use technological or digital tools in order to attract and motivate their students to work on mathematical tasks that are expected to be related to their future orientation