Abstract
Portugal made a climate commitment when it ratified the Paris Climate Agreement in 2015. As a result, Portugal, along with other EU members, has created a national roadmap for the deployment of hydrogen as a crucial component of Portugal ' s energy transition towards carbon neutrality, creating synergies between the electric and gas systems. The increased variability of generation from variable renewable power sources will create challenges regarding the security of supply, requiring investment in storage solutions to minimize renewable energy curtailment and to provide dispatchability to the electric power system. Hydrogen can be a renewable energy carrier capable of ensuring not only the desired transformation of the infrastructures of the gas system but also an integrator of the Electric System, such as in Power -to -Power (P2P) systems. Hydrogen can be produced with a surplus of renewable electricity from wind and solar, allowing a long-term energy seasonal storage strategy, namely by using underground salt caverns, to be subsequently transformed into electricity when demand cannot be supplied due to a shortage of renewable generation from solar or wind. P2P investments are capital intensive and require the development of transitional regulation mechanisms to both create opportunities to market agents while fostering the energy surplus valuation and decreasing the energy dependency. In order to maintain the electric system ' s security of supply, the suggested methodology innovatively manages the importance of seasonal storage of renewable energy surplus using hydrogen in power systems. It suggests a novel set of regulatory strategies to foster the creation of a P2P solution that maintains generation adequacy while assisting in decarbonising the electric power industry. Such methodology combines long-term adequacy assessment with regulatory framework evaluation to evaluate the cost of the proposed solutions to the energy system. A case study based on the Portuguese power system outlook between 2030 and 2040 demonstrates that the considerable renewable energy surplus can be stored as hydrogen and converted back into electricity to assure adequate security of supply levels throughout the year with economic feasibility under distinct public policy models.

Abstract
Higher Education Institutions (HEIs) are increasingly incorporating artificial i ntelligence (AI) into their learning setup. In this paper, we analyze the results of a survey posed to 152 Higher Education (HE) students and 136 HE educators, of different scientific b ackgrounds, to emphasize the current incorporation of AI in the teaching and learning processes. The results reveal distinct viewpoints from both parties, reflecting diversified l evels o f e xperience, presumptions, and uneasiness. Thirty two percent of the teachers, completing the survey, confirms using AI. Approximately 50% reveal they notice their students using AI to (i) automate routine tasks in or out-ofclass, including check correctness of answers, obtaining real-time feedback; (ii) personalize learning tasks, such as write essays or projects and to illustrate them, and create presentations. A smaller percentage reveals students using AI to produce video content and contrast information learned in class. Alternative means, encompassing using AI at home, to study, to gather information, to sum up ideas in texts, are identified by most teachers as being employed by their students. Students using AI outnumber the teachers, though there are significant d ifferences in some responses, when compared to the teachers' perceptions, for the sames questions. Most of the students prefer AI to study at home, to obtain information to improve or to check an answer. Then a significant number does not exploit AI either to create presentations, write an essay or project, illustrate a project, producing videos, or to contrast information obtained in classes with that collected by AI tools. Regardless of these differences, both parties agree and strongly agree (with 79% of students and 86% of teachers) that AI will affect the HEIs educational process in the future. © 2024 IEEE.

Abstract
High-frequency (HF) emissions, referred to as supraharmonics (SHs), are proliferating in low- and medium-voltage networks due to the increasing use of technologies that generate distortions in the 2 kHz to 150 kHz range. The propagation of SHs through the electrical grid causes interference with power supply components and end-user equipment. With the increasing frequency of these incidents, it is imperative to establish guidelines and regulations that facilitate diagnosis and limit the amount of emissions injected into the electrical grid. The proliferation of SH emissions from active power electronics devices is a significant concern, especially considering the growing importance of photovoltaic (PV) systems in the context of climate change. The aim of this paper is to address and analyze the emissions from different PV inverters present in an electrical network. Several scenarios were simulated to understanding and identifying possible correlations. This study examines real signals from PV systems, which exhibit narrowband, broadband and time-varying emissions. This paper concludes by emphasizing the need for specific regulations for this frequency range while also providing indications for future research.

Abstract
Context. The deployment of meter-scale (hitherto pre-focal) adaptive deformable mirrors finds some prominent examples in the leading ground-based visible to near-infrared facilities (e.g. the Very Large Telescope (VLT), the Large Binocular Telescope (LBT), or the Magellan Telescope) and is being adopted by several others (e.g. the Multiple Mirror Telescope (MMT) or Subaru). Furthermore, two out of the three giant segmented-mirror telescopes now under design will feature them. In all these cases, the proprietary technology is based on voice-coils and is limited in force, stroke, and velocity. Aims. Because of the nature of their purpose, that is, adaptive wave-front correction, any kind of optimality relies on the control of a subset of principal wave-front components or eigenmodes, for short, a basis of functions in a mathematical sense. Here we provide algorithmic procedures for generating such eigenbases, also called Karhunen–Loève (KL) modes, that integrate force limitations in their definitions whilst maintaining standard orthonormality, statistical independence, and deformable mirror span. Methods. The double-diagonalisation method was revisited to build KL modes ranked by the force applied on the actuators. Results. We analysed this new KL basis for von Kármán turbulence statistics and present the fitting error and the distribution of positions and forces. We further illustrate their use in the case of the quaternary mirror control for the European Extremely Large Telescope, and we include the outer actuator minioning and force policy constraints. © The Authors 2024.

Abstract

Abstract
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.