Abstract
Since its appearance in 2008, blockchain technology has found multiple uses in fields such as banking, supply chain management, and healthcare. One of the most intriguing uses of blockchain is in voting systems, where the technology can overcome the security and transparency concerns that plague traditional voting systems. This paper provides a thorough examination of the implementation of a blockchain-based voting system. The proposed system employs cryptographic methods to protect voters' privacy and anonymity while ensuring the verifiability and integrity of election results. Digital signatures, homomorphic encryption (He), zero-knowledge proofs (ZKPs), and the Byzantine fault-tolerant consensus method underpin the system. A review of the literature on the use of blockchain technology for voting systems supports the analysis and the technical and logistical constraints connected with implementing the suggested system. The study suggests solutions to problems such as managing voter identification and authentication, ensuring accessibility for all voters, and dealing with network latency and scalability. The suggested blockchain-based voting system can provide a safe and transparent platform for casting and counting votes, ensuring election results' privacy, anonymity, and verifiability. The implementation of blockchain technology can overcome traditional voting systems' security and transparency shortcomings while also delivering a high level of integrity and traceability.

Abstract
Renewable energy communities (REC) are an increasingly interesting solution for all energy market stakeholders. In RECs consumers and producers come together to form energy cooperatives with a strong incorporation of renewables in order to make the market and energy trading more advantageous for both sides. This growing trend has been followed by several studies aimed at understanding which are the best models for energy sharing within the community. This paper proposes different models of energy sharing within the community and evaluates their efficiency. Energy sharing can be based on constant coefficients or variable coefficients based on the net consumption of the self-consumers. This study proposes a new methodology based on a hybrid model. The results show the advantages and challenges of the individual energy-sharing models, showing that up to 41% of the energy imports from the grid can be reduced. © 2023 IEEE.

Abstract
The use of graphene oxide (GO) as a saturable absorber for short pulses generation in an Erbium-doped fiber laser was studied and demonstrated. The saturable absorber consisted of a thin GO film, with a high concentration of monolayer GO flakes, spray-coated on the end face of a ferrule-connected fiber. By including the saturable absorber in the laser cavity and controlling the intra-cavity polarization, the generation of shortpulsed light was achieved under mode-locking and Q-switching operations. Under mode-locking operation, it was observed a pulse train with a fundamental repetition rate of 1.48 MHz, with a working wavelength centered at 1564.4 nm. In the Q-switch operation, a pulse train with a 12.7 kHz repetition rate and a 14.3 µs pulse duration was attained for a 230-mA pump current. Further investigation showed a linear dependence of the repetition rate with the pump power, attaining frequencies between 12.7 and 14.4 kHz. © 2023 by SCITEPRESS - Science and Technology Publications, Lda.

Abstract
Zero-shot learning enables the recognition of classes not seen during training through the use of semantic information comprising a visual description of the class either in textual or attribute form. Despite the advances in the performance of zero-shot learning methods, most of the works do not explicitly exploit the correlation between the visual attributes of the image and their corresponding semantic attributes for learning discriminative visual features. In this paper, we introduce an attention-based strategy for deriving features from the image regions regarding the most prominent attributes of the image class. In particular, we train a Convolutional Neural Network (CNN) for image attribute prediction and use a gradient-weighted method for deriving the attention activation maps of the most salient image attributes. These maps are then incorporated into the feature extraction process of Zero-Shot Learning (ZSL) approaches for improving the discriminability of the features produced through the implicit inclusion of semantic information. For experimental validation, the performance of state-of-the-art ZSL methods was determined using features with and without the proposed attention model. Surprisingly, we discover that the proposed strategy degrades the performance of ZSL methods in classical ZSL datasets (AWA2), but it can significantly improve performance when using face datasets. Our experiments show that these results are a consequence of the interpretability of the dataset attributes, suggesting that existing ZSL datasets attributes are, in most cases, difficult to be identifiable in the image. Source code is available at https://github.com/CristianoPatricio/SGAM.

Abstract
Gamma radiation over the Atlantic Ocean was measured continuously from January to May 2020 by a NaI(Tl) detector installed on board the Portuguese navy's ship NRP Sagres. Enhancements in the gamma radiation values are identified automatically by an algorithm for detection of anomalies in mean and variance as well as by visual inspection. The anomalies are typically +50% above the background level and relatively rare events (similar to<10% of the days). All the detected anomalies are associated with simultaneous precipitation events, consistent with the wet deposition of scavenged radionuclides. The enhancements are detected in the open ocean even at large distances (+500 km) from the nearest coastline. Back trajectories reveal that half of these events are associated with air masses experiencing continental land influences, but the other half do not display evidence of recent land contact. The enhancements in gamma radiation very far from land and with no evidence of continental fetch from back trajectories are difficult to explain as resulting only from radionuclides with a terrestrial source such as radon and its progeny. Further investigation and additional measurements are needed to improve understanding on the sources of ambient radioactivity in the open ocean and assess whether gamma radiation in the marine environment is influenced not only by radionuclides of terrestrial origin, but also cosmogenic radionuclides, like Beryllium-7, formed in the upper atmosphere but with the ability to be transported downward and serve as a tracer of the aerosols to which it attaches. Plain Language Summary Radioactive elements such as the noble gas radon and those produced by its radioactive decay are naturally present in the environment and used as tracers of atmospheric transport and composition. In particular, the noble gas radon, being inert and of predominantly terrestrial origin, is used to identify pristine marine air masses with no land contamination. Precipitation over land typically brings radon from the atmosphere to the surface, enhancing gamma radiation on the ground, but such enhancements have not been identified before nor expected over the ocean due to the low amount of radon typical of marine air masses. Here we report, for the first time, gamma radiation enhancements associated with precipitation in the oceanic environment, using measurements performed over the Atlantic Ocean in a campaign onboard the Portuguese navy ship NRP Sagres.

Abstract
The teaching of entrepreneurship has been progressively included in the curricula of several university courses to stimulate the development of empowering attitudes and an entrepreneurial mentality. However, a new form of entrepreneurship has emerged with a focus on sustainability and the creation of new projects that aim to reduce social asymmetries and contribute to a fairer and more balanced society. The role of universities is also to foster the emergence of these projects through the implementation of practices aimed at fostering social entrepreneurship among students. This study aims to understand the determinant dimensions that characterise the students' social entrepreneurial intention. For this purpose, a sample of 177 students attending a social entrepreneurship course in a higher education institution was employed. The findings indicate that individual, organisation, and context constructs are determinants of students' entrepreneurial intention. However, not all organisational factors contribute equally. Mentoring and social networks are relevant elements for the entrepreneurial intention of individuals, while curriculum and critical pedagogy are not recognised as determinants.