Abstract
This article addresses the significant issue of job deterioration effects in job-shop scheduling problems and aims to create awareness on its impact within the manufacturing industry. While previous studies have explored deteriorating effects in various production configurations, research on scheduling problems in complex settings, particularly job-shop, is very limited. Thus, we address and optimize the impact of job deterioration in a generic job-shop scheduling problem (JSP). The JSP with job deterioration is harder than the classical JSP as the processing time of an operation is only known when the operation is started. Hence, we propose a biased random key genetic algorithm to find good quality solutions quickly. Through computational experiments, the effectiveness of the algorithm and its multi-population variant is demonstrated. Further, we investigate several deterioration functions, including linear, exponential, and sigmoid. Job deterioration increases operations' processing time, which leads to an increase in the total production time (makespan). Therefore, the management should not ignore deterioration effects as they may lead to a decrease in productivity, to an increase in production time, costs, and waste production, as well to a deterioration in the customer relations due to frequent disruptions and delays. Finally, the computational results reported clearly show that the proposed approach is capable of mitigating (almost nullifying) such impacts. © 2023 IEEE.

Abstract
In a global world, within the context of an unprecedented climate crisis, STEM education may decisively contribute to sustainable economic growth. Within this context, Portugal has been following EU guidelines by creating programs that encourage students and teachers to envision STEM education with innovative methodologies. Today's schools should provide students with motivating approaches to increase their interest in enrolling STEM courses and professions. Despite all the efforts, the results are still far from the desired goals in some areas, such as electrical engineering. In this paper, an articulation program between Coimbra Institute of Engineering (ISEC), secondary schools, and a Private Social Solidarity Institution - CASPAE, being developed under the PO ISE Program, co-financed by EU, is described. The main objective of this program is to promote STEM subjects next to young students, in an interesting, experimental and interactive environment. The program proposes several experiments that are closely related to renewable energies and sustainable energy use, in which STEM knowledge is mandatory. With technical support from ISEC, a set of interactive experiments of real-world problems was developed. To make each experiment more appealing, a multidisciplinary approach was used, bringing together experts in electrical engineering, computer science, and art designers. Four experiments called "Energy Rivers", "A Breath of Wind", "Sun Flower"and "Kilometer by Kilometer"integrate a physical prototype and a simulator of a Hydroelectric Power Plant, a Photovoltaic Panel, a Wind Turbine, and an Electrical Vehicle, respectively. Each experiment is integrated into an attractive design that suggests the purpose of the experiment, enclosed in portable modules. The set of experiments will travel to schools, thus increasing the project audience target. © 2023 Fontys University of Applied Science.

Abstract
Objective: Murmurs are abnormal heart sounds, identified by experts through cardiac auscultation. The murmur grade, a quantitative measure of the murmur intensity, is strongly correlated with the patient's clinical condition. This work aims to estimate each patient's murmur grade (i.e., absent, soft, loud) from multiple auscultation location phonocardiograms (PCGs) of a large population of pediatric patients from a low-resource rural area. Methods: The Mel spectrogram representation of each PCG recording is given to an ensemble of 15 convolutional residual neural networks with channel-wise attention mechanisms to classify each PCG recording. The final murmur grade for each patient is derived based on the proposed decision rule and considering all estimated labels for available recordings. The proposed method is cross-validated on a dataset consisting of 3456 PCG recordings from 1007 patients using a stratified ten-fold cross-validation. Additionally, the method was tested on a hidden test set comprised of 1538 PCG recordings from 442 patients. Results: The overall cross-validation performances for patient-level murmur gradings are 86.3% and 81.6% in terms of the unweighted average of sensitivities and F1-scores, respectively. The sensitivities (and F1-scores) for absent, soft, and loud murmurs are 90.7% (93.6%), 75.8% (66.8%), and 92.3% (84.2%), respectively. On the test set, the algorithm achieves an unweighted average of sensitivities of 80.4% and an F1-score of 75.8%. Conclusions: This study provides a potential approach for algorithmic pre-screening in low-resource settings with relatively high expert screening costs. Significance: The proposed method represents a significant step beyond detection of murmurs, providing characterization of intensity, which may provide an enhanced classification of clinical outcomes.

Abstract
The growing demand for multiparameter sensors includes compact devices accompanied by simple calibration processes to distinguish the outputs from each other. This paper evaluates a scheme to determine multiple parameters of a medium using localized surface plasmon resonances (SPR) excited on a Dshaped photonic crystal fiber (PCF) partially covered by two gold layers of different thicknesses. We demonstrate that the proposed sensing platform, once customized to characterize the possible dispersive profiles of the refractive index of the analyte, also allows interrogating the temperature of a sample from a linear relationship. Since the plasmonic resonances are excited at separated and low crosstalk spectral channels, different sensing responses can be obtained simultaneously in the same location of the D-shaped PCF. These features turn out the SPR sensor a suitable tool for simultaneous monitoring of optical dispersion and temperature. © 2023 SBMO/SBMag.

Abstract
The pose estimation of a mobile robotic system is essential in many autonomous applications. Inertial sensors provide high-frequency measurements that can be used to estimate the displacement, however, for estimating the orientation, an additional filter is required. Some of the newest Attitude and Heading Reference Systems can provide a referenced estimation of the orientation of the device, allowing it to retrieve the orientation of a robotic system. However, magnetic field perturbations caused by ferromagnetic objects or induced magnetic fields might influence these systems and, consequently, lead to the accumulation of errors over time. In this paper, the performance of the Xsens fusion filter is compared with a stateof-the-art algorithm to estimate the orientation of the system under dynamic movements and in the presence of magnetic perturbations, with the goal of finding the most suitable for an Unmanned Aerial Vehicle. The results show that both filters are robust and perform well in the target scenario, with a root mean squared error between 2 and 5 degrees; however, the Xsens fusion filter does not require an extra computer to process the data.

Abstract
Topics discussed on social media platforms contain a disparate amount of information written in colloquial language, making it difficult to understand the narrative of the topic. In this paper, we take a step forward, towards the resolution of this problem by proposing a framework that performs the automatic extraction of narratives from a document, such as tweet posts. To this regard, we propose a methodology that extracts information from the texts through a pipeline of tasks, such as co-reference resolution and the extraction of entity relations. The result of this process is embedded into an annotation file to be used by subsequent operations, such as visualization schemas. We named this framework Tweet2Story and measured its effectiveness under an evaluation schema that involved three different aspects: (i) as an Open Information extraction (OpenIE) task, (ii) by comparing the narratives of manually annotated news articles linked to tweets about the same topic and (iii) by comparing their knowledge graphs, produced by the narratives, in a qualitative way. The results obtained show a high precision and a moderate recall, on par with other OpenIE state-of-the-art frameworks and confirm that the narratives can be extracted from small texts. Furthermore, we show that the narrative can be visualized in an easily understandable way.