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.

Abstract

Abstract
Schizophrenia is a mental illness that affects an estimated 21 million people worldwide. The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate possible complications. Therefore, it is necessary to identify disease-specific biomarkers for an early diagnosis support system. This work contributes to improving our knowledge about schizophrenia and the features that can identify this mental illness via speech and EEG. The emotional state is a specific characteristic of schizophrenia that can be identified with speech emotion analysis. The most used features of speech found in the literature review are fundamental frequency (F0), intensity/loudness (I), frequency formants (F1, F2, and F3), Mel-frequency cepstral coefficients (MFCC's), the duration of pauses and sentences (SD), and the duration of silence between words. Combining at least two feature categories achieved high accuracy in the schizophrenia classification. Prosodic and spectral or temporal features achieved the highest accuracy. The work with higher accuracy used the prosodic and spectral features QEVA, SDVV, and SSDL, which were derived from the F0 and spectrogram. The emotional state can be identified with most of the features previously mentioned (F0, I, F1, F2, F3, MFCCs, and SD), linear prediction cepstral coefficients (LPCC), linear spectral features (LSF), and the pause rate. Using the event-related potentials (ERP), the most promissory features found in the literature are mismatch negativity (MMN), P2, P3, P50, N1, and N2. The EEG features with higher accuracy in schizophrenia classification subjects are the nonlinear features, such as Cx, HFD, and Lya.