Abstract
This paper investigates the stability of a converter-dominated islanded power system when the island’s battery energy storage converters are operated in different control modes (Grid Forming and Grid Following) and combined with different volumes of synchronous compensation. The study is conducted in a realistic simulation model of the future Madeira island, where no thermal generation is present, and the share of converter-based Renewable Energy Sources is large (75 to 80 % of instantaneous penetration). The impact of the different combinations of synchronous condensers and BESS converter control modes on the system stability is evaluated using a stability index-based approach that accounts for multiple operation scenarios. In this procedure, the system’s dynamic response to the reference disturbances (short-circuits in the Transmission and Distribution Network) is obtained via RMS dynamic simulation and is then analyzed to extract two stability indices (Nadir and Rocof). Such indices are computed for the synchronous generator speed and the grid electrical frequency (measured in different points using a PLL) and are later used as the basis for discussion and conclusion drawing. © Energynautics GmbH.

Abstract
A novel non-destructive analytical method for early diagnosis of two bacterial diseases, Pseudomonas syringae and Xanthomonas euvesicatoria, in tomato plants, using ultraviolet-visible (UV-Vis) transmittance spectroscopy and chemometric models, is developed. Plant-pathogen interactions caused tissue damage that generated non-linear data patterns compared to the control set (healthy samples), which challenges traditional discrimination models, even when employing non-linear discriminant approaches. Alternatively, an authentication task to conduct oneclass classification relying on a data-driven version of soft independent modeling of class analogy (DD-SIMCA) is a wise choice due to its quadratic approach, proper to deal with non-linear data. DD-SIMCA detached the target class (control healthy plant leaflet tissues) from all other samples (target class and non-target class of plant leaflet tissues inoculated with two bacteria, even before the manifestation of macroscopic lesions associated with the diseases) by capturing the main similarities within the samples of the target class through the full distance that acts as a classification analytical signal, reaching 100 % sensitivity in the training and validation sets. Multivariate curve resolution - alternating least-squares (MCR-ALS) constrained analysis allowed the description of the bacterial inoculation process on diseased tissues through pure spectral signatures. DD-SIMCA results indicate that non-target class of samples with higher proximity to the acceptance boundary suggested that they were at earlier stages of infection when compared to more distant ones, presenting lower full distance values. These findings reveal that a handheld UV-Vis transmittance spectrometer is sufficiently sensitive to be used in acquiring biological data with suitable chemometric models for early disease diagnosis and prompt intervention.

Abstract
Our current society faces challenges in integrating individuals with disabilities, making this process difficult and painful. People with disabilities (PwD) are often mistakenly considered incapable due to the difficulties they face in daily tasks due to the lack of adapted means and tools. In this context, assistive technologies play a crucial role in improving the quality of life for these individuals. However, assistive technologies still have various limitations, making research in this area essential to enhance existing solutions and develop new approaches that meet individual needs, aiming to promote inclusion and equal opportunities. This paper presents a research project that focuses on the study of electromyography (EMG) signal processing generated by individuals who have undergone amputations. These signals are essential in assistive technologies, such as myoelectric prostheses. The study focuses on the impact of different filters and machine learning training methods on this processing. The results of this study have the potential to provide relevant findings for the development of more efficient assistive technologies. By understanding the processing of EMG signals and applying machine learning techniques, it is possible to improve the accuracy and response speed of prosthetics, increasing the functionality and naturalness of movements performed by users, as well as paving the way for the emergence of new technologies.

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).