Abstract
Laboratory experiments are important pedagogical tools in engineering courses. Restrictions related to the COVID-19 pandemic made it very difficult or impossible for laboratory classes to take place, resulting on a fast transition to simulation as an approach to guarantee the effectiveness of teaching. Simulation environments are powerful tools that can be adopted for remote classes and self-study. With these tools, students can perform experiments and, in some cases, make use of the laboratory facilities from outside of the University. This paper proposes and describes two free tools developed during the COVID-19 pandemic lock-down that allowed students to work from home, namely a set of simulation experiments and a Hardware-in-the-loop simulator, accessible 24/7. Two approaches in Python and C languages are presented, both in the context of Robotics courses for Engineering students. Successful results and student feedback indicate the effectiveness of the proposed approaches in institutions in Portugal and in the Netherlands.

Abstract
Laser-induced breakdown spectroscopy is a spectroscopic technique that allows for fast elemental mapping of heterogeneous samples. Yet, detailed maps need high-resolution sampling grids, which can turn the task into a time-consuming process and can increase sample damage. In this work, we present the implementation of an imaged-based intelligent mesh algorithm that makes use of superpixel segmentation to optimize elemental mapping processes. Our results show that the approach can increase the elemental mapping resolution and decrease acquisition times, fostering opportunities for applications that benefit from minimal sample damage such as heritage analysis, or timely analysis such as industrial applications. © 2022 The Author(s)

Abstract
The motion of S2, one of the stars closest to the Galactic Centre, has been measured accurately and used to study the compact object at the centre of the Milky Way. It is commonly accepted that this object is a supermassive black hole, but the nature of its environment is open to discussion. Here, we investigate the possibility that dark matter in the form of an ultralight scalar field 'cloud' clusters around Sgr A*. We use the available data for S2 to perform a Markov Chain Monte Carlo analysis and find the best-fit estimates for a scalar cloud structure. Our results show no substantial evidence for such structures. When the cloud size is on the order of the size of the orbit of S2, we are able to constrain its mass to be smaller than 0.1 % of the central mass, setting a strong bound on the presence of new fields in the galactic centre.

Abstract
Almond trees in Portugal are susceptible to aphid infestation, which can result in reduced fruit production. To effectively tackle this issue, the combination of remote sensing (RS) data and machine learning (ML) classifiers can be used to accurately detect the presence of aphids. This study focuses in the implementation of ML classifiers and RS data analysis to identify aphids on almond trees, using high-resolution multispectral data collected through an unmanned aerial vehicle (UAV) in a Portuguese almond orchard. Four ML classifiers, kNN, SVM, RF and XGBoost, were employed and fine-tuned using vegetation indices derived from spectral data. The results revealed that the SVM classifier achieved an overall accuracy (OA) of 77%, followed by kNN with an OA of 74%, while XGBoost and RF achieved OAs of 71% and 69%, respectively. Consequently, this study demonstrates the viability of employing RS data and ML classifiers for aphid identification in almond orchards.

Abstract
Increasing digitalization has posed new challenges for businesses, and digital coupons are an important means of promoting their sales. However, there are still gaps in the literature on how they are distributed. The objective of this paper is to study whether the distribution of digital coupons through a referral program increases purchase intention and perceived quality towards a product. By conducting an experimental design, the results point out that consumer purchase intention increases if the recommendation is made by someone with a strong relationship and if a digital coupon is offered, and when the tie relationship is weak or no relationship, it does not vary significantly. On the other hand, the results showed that perceived quality does not vary with the offer of a digital coupon, regardless of the strength of the tie between the person who recommended and the consumer. Current research suggests that managers should use this information to design a digital coupon program tailored to the company’s objectives to retain and capture customers. This new approach to digital voucher distribution is one of the first to investigate their distribution, and their simultaneous use with a referral program. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Integration of prosumers in district heating networks brings new challenges to the market and the network operation since they can change the thermal flow and increase competition. Thus, it is mandatory to develop new market structures and network management mechanisms. In this scope, this work proposes the implementation of a coordination methodology based on a peer-to-peer market without a supervising entity. The goal is to achieve higher revenue by coping with the requirements of each agent. Furthermore, the model is validated through network nodal analysis inspired by the power sector. The results in a Nordic network point out that the coordination methodology can provide compromise solutions between market negotiation and network operation. This methodology succeeded in providing reliable network solutions, fixing 99.88% of network burdens just after one iteration, and encouraging prosumers' integration. This increases market competition which lowers the energy costs for consumers while avoiding the network's operating burdens.