Abstract
In recent years, technologies related to the electrification of transportation have attracted significant attention. Among these, wireless charging stands out, even facing numerous challenges concerning design and parameter optimization. Consequently, this article introduces a novel design methodology to improve the performance of inductive power transfer (IPT) systems for wireless charging applications in electric vehicles. The methodology considers operational limits of switches and passive components. By using a combination of Newton-Raphson and Particle Swarm Optimization (PSO) algorithms, the proposed approach efficiently determines both electrical and physical parameters of converters and coils to achieve maximum efficiency at a chosen operational point. Furthermore, a Maximum Efficiency Point Tracking (MEPT) algorithm is employed for optimal system operation. The proposed methodology is validated through experimental analysis using a 3.6 kW setup. Results demonstrate a power transfer efficiency around 89.4 %, while ensuring that current and voltage levels remain within safe operating areas for the components.

Abstract
The paper presents the comparison of accuracy in the Speech Emotion Recognition task using the Hamming and Hanning windows for framing the speech and determining the spectrogram to be used as input of a convolutional neural network. The detection of between 4 and 10 emotional states was tested for both windows. The results show significant differences in accuracy between the two window types and provide valuable insights for the development of more efficient emotional state detection systems. The best accuracy between 4 and 10 emotions was 64.1% (4 emotions), 57.8% (5 emotions), 59.8% (6 emotions), 48.4% (7 emotions), 47.8% (8 emotions), 51.4% (9 emotions), and 45.9% (10 emotions). These accuracy is at the state-of-the art level.

Abstract
The transition to college is a period of higher risk of the development of eating disorders, with nutrition/dietetics students representing a group of particular vulnerability. Hence, it is interesting to assess eating disorders, taking into consideration potential sources of bias, including social desirability. Our aims were to compare the risk of eating disorders between students of nutrition/dietetics and those attending other courses and to study potential social desirability biases. A total of 799 higher education students (81.7% females) aged 18 to 27 years old completed a questionnaire assessing the risk of eating disorders (EAT-26) and social desirability (composite version of the Marlowe-Crowne Social Desirability Scale). The proportion of students with a high risk of eating disorders was higher among females (14.5% vs. 8.2%, p = 0.044). Nutrition/dietetics students did not differ from those attending other courses regarding the risk of eating disorders. The social desirability bias when assessing the risk of eating disorders was overall low (EAT-26 total score: r = -0.080, p = 0.024). Social desirability correlated negatively with the Diet (r = -0.129, p < 0.001) and Bulimia and food preoccupation subscales (r = -0.180, p < 0.001) and positively with Oral self-control (r = 0.139, p < 0.001).

Abstract
During the process of software architectural design, numerous questions arise which must be answered. These questions may be about requirements on the proposed system (the problem space) or about how the system should be designed and developed (the solution space). As questions arise they may be answered immediately, deferred until later, or provisionally answered with an assumption about the answer. The objective of this work was to explore the nature of questions that arise during architecture. We explored the types of questions, how they are organized, how they are tracked, and how and when they are answered. We started by surveying highly experienced architects about their practices with respect to architectural questions. We also performed a controlled experiment with master students about organizing architectural questions that clarified and substantiated the survey data. We learned that architectural questions include slightly more questions about the problem space than the solution space, as well as a minority of questions related to the managing of the project. We found that architects often use ad hoc methods to organize and track them, although they typically organize them along more than one dimension. We learned also that, about a third of the time, architects make assumptions about the answers to architectural questions in order to make progress on the architecture. This suggests that some projects may have risks of incorrect design or later costly rework due to inadequate tracking or incorrectly answered architectural questions.

Abstract
RGB-D sensors face multiple challenges operating under open-field environments because of their sensitivity to external perturbations such as radiation or rain. Multiple works are approaching the challenge of perceiving the three-dimensional (3D) position of objects using monocular cameras. However, most of these works focus mainly on deep learning-based solutions, which are complex, data-driven, and difficult to predict. So, we aim to approach the problem of predicting the three-dimensional (3D) objects’ position using a Gaussian viewpoint estimator named best viewpoint estimator (BVE), powered by an extended Kalman filter (EKF). The algorithm proved efficient on the tasks and reached a maximum average Euclidean error of about 32mm. The experiments were deployed and evaluated in MATLAB using artificial Gaussian noise. Future work aims to implement the system in a robotic system. © 2024 by SCITEPRESS-Science and Technology Publications, Lda.

Abstract
There is evidence that some outdoor events may have contributed to the spread of COVID-19. We updated an empirical methodology based on regression modeling and hypothesis testing to analyze the potential impact of a demonstration that took place in Lisbon, within the scope of the ’Black Lives Matter’ context, on the contagion pattern in the region where this event occurred. We find that in the post-impact period there was no acceleration in the number of cases in the region, unlike in a prior event in the region. The proportion of counties where there was a potential impact of the event is not statistically significant. This result demonstrates that not all outdoor events contributed to the spread of COVID-19 and exemplifies how to apply the selected empirical methodology.