Abstract
Metaplasia detection in upper gastrointestinal endoscopy is crucial to identify patients at higher risk of gastric cancer. Deep learning algorithms can be useful for detecting and localising these lesions during an endoscopy exam. However, to train these types of models, a lot of annotated data is needed, which can be a problem in the medical field. To overcome this, data augmentation techniques are commonly applied to increase the dataset's variability but need to be adapted to the specificities of the application scenario. In this study, we discuss the potential benefits and identify four key research challenges of a promising data augmentation approach, namely image combination methodologies, such as CutMix, for metaplasia detection and localisation in gastric endoscopy imaging modalities.

Abstract
The Atacama desert is a region with exceptional conditions for solar power production. However, despite its relevance, the impact of climate change on this resource in this region has barely been studied. Here, we use regional climate models to explore how climate change will affect the photovoltaic solar power resource per square meter (PVres) in Atacama. Models project average reductions in PVres of 1.5% and 1.7% under an RCP8.5 scenario, respectively, for 2021-2040 and 2041-2060. Under RCP2.6 and the same periods, reductions range between 1.2% and 0.5%. Also, we study the contribution to future changes in PVres of the downwelling shortwave radiation, air temperature and wind velocity. We find that the contribution from changes in wind velocity is negligible. Future changes of downwelling shortwave radiation, under the RCP8.5 scenario, cause up to 87% of the decrease of PVres for 2021-2040 and 84% for 2041-2060. Rising temperatures due to climate change are responsible for drops in PVres ranging between 13%–19% under RCP2.6 and 14%–16% under RCP8.5. We conclude that climate change has the potential to impact the PVres in the Atacama region while retaining exceptional conditions for solar power production.

Abstract
The increasing use of wearable electronics calls for sustainable energy solutions. Biomechanical energy harvesting appears as an attractive solution to replace the use of batteries in wearables, as the body generates sufficient power to drive small electronics. In particular, triboelectric nanogenerators (TENGs) have emerged as a promising approach due to its lightweight and high power density. In this work, a TENG is hybridized with an electromagnetic generator (EMG) to harvest energy from the foot strike. An enclosed radial-flow turbine is optimized and used to convert the foot-strike low-frequency linear movement into a higher-frequency rotational motion (by a factor of & AP;12). Besides increasing the motion frequency, the employed mechanism is physically robust and enables a continuous operation from irregular mechanical excitations. A single TENG unit operating in the freestanding mode generated an optimal power of 4.72 & mu;W and transferred a short-circuit charge of 2.3 nC. The TENG+EMG hybridization allows to power a digital pedometer even after the mechanical input stopped. Finally, the energy harvester is incorporated into a commercial shoe to power the same pedometer from foot walking. The obtained results validate the developed prototype ability to serve as a portable power source that can drive sensors and wearable electronics.

Abstract
The recognition of Covid-19 as a global pandemic in March 2020 forced the closure of schools and universities around the world, raising the need to adopt emergency teaching methods. A year and a half later, the situation is still not resolved, but there is more data that allow us to understand the real impact. This study presents a comprehensive analysis of higher education students perceptions about courses and faculty during the last 5 years (2016-2021), with a special focus on the differences in perception between the pre-Covid-19 and the during Covid-19 phases. To this end, the pedagogical surveys that are answered by students from an engineering degree at a Portuguese university at the end of the first and second semester of the academic year are analyzed. The results allow us to identify two distinct moments in the Covid-19 phase: a first in which feelings of positivism and appreciation of students for the instructors and the courses they teach stand out, and a second moment in which students become more demanding and dissatisfied with the courses and with the instructors, leading to a lack of motivation and involvement of students. © 2023, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract
Implementing Artificial Reefs (AR) is seen as a worldwide strategy to overcome the problematic environmental impacts due to climate change, overfishing and other activities that damage natural habitats and ecosystems. The production and deployment of ARs involve a series of technical challenges and this work explores a possible alternative solution to overcome some of these challenges. This work addresses a new system for building underwater modular structures integrating monitoring sensors, exploring the potential that Flat Knitted textiles have when specifically designed and used as Knitted Textile Preforms (KTPs) for concrete filling. The casting of AR modules directly in their final position, underwater, brings new paradigms to the design and production of ARs clusters with more complex geometries and geometrical continuity between modules. This manuscript is dedicated to the analysis of variables such as injection pressure and its variations during filling and curing, respectively, as well as the textile structure and geometrical design. It also addresses the possibilities of the technology to embed environmental sensors, which will add functions to the AR structure.

Abstract
Trade promotions are complex marketing agreements between a retailer and a manufacturer aiming to drive up sales. The retailer proposes numerous sales promotions that the manufacturer partially supports through discounts and deductions. In the Portuguese consumer packaged goods (CPG) sector, the proportion of price-promoted sales to regular-priced sales has increased significantly, making proper promotional planning crucial in ensuring manufacturer margins. In this context, a decision support system was developed to aid in the promotional planning process of two key product categories of a Portuguese CPG manufacturer. This system allows the manufacturer’s commercial team to plan and simulate promotional scenarios to better evaluate a proposed trade promotion and negotiate its terms. The simulation is powered by multiple gradient boosting machine models that estimate sales for a given promotion based solely on the scarce data available to the manufacturer. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.