Abstract
Carlos Baquero and Rosa Cabecinhas consider how readers make assumptions about authors’ roles and relative contributions when reading papers. It is not unexpected that when reading papers, readers also make simplifications and have assumptions about author roles and relative contributions. Experts also observed that the success of a new work depends not only on its factual quality, but on the prior recognition of the author and its institution. Work done at more prestigious departments can diffuse more rapidly through the science networks. The bias that occurs both on author and institution recognition is now well-known and a justification for blind review mechanisms.

Abstract
Este artigo apresenta e discute uma prática pedagógica cocriada no contexto do componente curricular Seminário Temático “Educação & Transformação Digital”, um dos produtos que tem origem na relação entre duas pesquisas desenvolvidas em nível de internacionalização: “Transformação Digital e Humanidades: Educação e Comunicação em Movimento”(CAPES PrInt) e “O Urbano e o Pós-Urbano como Espaços de Aprendizagem: formação de professores e pesquisadores na cultura híbrida e multimodal” (FAPERGS). A Atividade Acadêmica é ofertada, conjuntamente, na modalidade on-line, para mestrandos e doutorandos dos programas de Pós-Graduação da Unisinos e da Universidade Aberta de Portugal (UAb). O referido seminário visou problematizar os desafios e cenários emergentes na área da Educação Digital e, também, como as diferentes instituições estão se apropriando das transformações digitais a fim de provocar inovações na Educação. A pesquisa é de natureza exploratória e descritiva, de abordagem qualitativa, e se apropria do Método Cartográfico de Pesquisa-Intervenção, para produção e análise de dados. Nesse sentido, como um dos resultados do percurso investigativo e problematizador realizado no Seminário, emergiu a vivência “Novas Aventuras de Dom Quixote”, uma prática pedagógica inventiva. Esta vivência, fundamentada nas Epistemologias Reticulares e Conectivas, foi cocriada na perspectiva de uma Educação online/onlife, objetivando a apropriação de diferentes Tecnologias Digitais (TD), no enfrentamento de desafios contemporâneos de aprendizagem e que buscou instigar a apropriação das mesmas, no sentido de compreendê-las enquanto potência, que pode contribuir para propiciar vivências de aprendizagem e a ampliação dos espaços educacionais, uma vez que possibilitam a transubstanciação da educação, do espaço físico das instituições educacionais, para o formato on-line.

Abstract
Determining the transformer top-oil temperature (TOT) is one of the key issues in determining the transformer insulation life and reliability of the power system. Due to the non-linear nature of the model presented in the IEEE C57.91 standard to determine this temperature, a more precise method is needed to estimate the equation coefficients to estimate the TOT in the future. This paper presents a method for online thermal modelling of the transformer according to the IEEE C57.91 based on the Unscented Kalman filter (UKF). This method can be applied to transformers with a variety of cooling modes and estimates the TOT with an acceptable error. In order to evaluate the proposed method, the practical data of the 800 kVA distribution transformer with unknown equation coefficients and simulated data with known coefficients are used, and finally, by calculating the estimation error, the proper performance of the presented method is proved. It is proved that the proposed method predicts TOT even in the presence of noise with an error of less than 0.5 degrees C and a delay of less than 1.5 h. It makes the proposed method can be implemented for purposes such as load management, and insulation life estimation of the transformer.

Abstract
The interconnection of AC and DC microgrids results in a hybrid AC/DC microgrid (HMG). In light of HMGs, the future smart grid implementation will be facilitated. One important aspect in HMGs is the interconnection of AC and DC microgrids and control of bidirectional interlink power converters (BILPCs), which has taken a lot of research attention in the last decade. The BILPCs are the most prevalent method for interconnection of HMGs. Thus, the current study first reviews different interconnection methods and control challenges of AC and DC microgrids in HMGs and then overviews various control strategies of BILPCs presented in literature, all carried out in a comprehensive manner.

Abstract
This article presents a novel mixed-integer second-order cone programming model to increase the photovoltaic (PV) hosting capacity and optimize the operation of distribution networks. The problem considers voltage and reactive (Volt/VAr) control through the optimal operation of capacitors banks, substations' on-load tap changers, voltage regulators, and network reconfiguration with radial and closed-loop operation topologies. The proposed formulation considers voltage-dependent models for loads and capacitor banks. The objective function maximizes the PV hosting capacity of the network. Numerical experiments are carried out using the 33-node and the 85-node networks. Results demonstrate the effectiveness of the proposed formulation to increase the penetration of PV sources, especially when the closed-loop operation is allowed, together with network reconfiguration and Volt/VAr control.

Abstract
In the healthcare domain, datasets are often private and lack large amounts of samples, making it difficult to cope with the inherent patient data heterogeneity. As an attempt to mitigate data scarcity, generative models are being used due to their ability to produce new data, using a dataset as a reference. However, synthesis studies often rely on a 2D representation of data, a seriously limited form of information when it comes to lung computed tomography scans where, for example, pathologies like nodules can manifest anywhere in the organ. Here, we develop a 3D Progressive Growing Generative Adversarial Network capable of generating thoracic CT volumes at a resolution of 1283, and analyze the model outputs through a quantitative metric (3D Muli-Scale Structural Similarity) and a Visual Turing Test. Clinical relevance - This paper is a novel application of the 3D PGGAN model to synthesize CT lung scans. This preliminary study focuses on synthesizing the entire volume of the lung rather than just the lung nodules. The synthesized data represent an attempt to mitigate data scarcity which is one of the major limitations to create learning models with good generalization in healthcare.