Abstract
The decrease of short-circuit current in islanded mode due to the existence of resources with low inertial, topology changing, multi-directional current, and telecommunication are the most critical issues encountered by micro-grid protection. The central adaptive protection schemes are widely used for micro-grids, but due to the lack of reliability of this schemes, they are not perfect methods. In this paper, a hybrid scheme of adaptive and multi-agent protection for micro-grid is discussed, which will be able to provide safety protection at several layers and levels, using the equipment in the micro-grid with distributed generation including renewable and nonrenewable energy resources. The proposed scheme calculates relays pickup current with a formula that uses the superposition theorem. To demonstrate the proposed scheme performance, it is implemented and simulated on a sample micro-grid in MATLAB/Simulink, and its results have been analyzed and discussed. Simulations and numerical results show that for 96% of simulated topologies in single and multi-event faults, the relay settings are updated correctly and detect subsequent faults at the right time. Also, due to the use of offline calculations in the equipment layer, the time delay due to sending information to higher layers is minimized for single-event faults in the micro-grid. (C)& nbsp;2022 Published by Elsevier Ltd.

Abstract
The coronavirus disease 2019 (COVID-19) evolved into a global pandemic, responsible for a significant number of infections and deaths. In this scenario, point-of-care ultrasound (POCUS) has emerged as a viable and safe imaging modality. Computer vision (CV) solutions have been proposed to aid clinicians in POCUS image interpretation, namely detection/segmentation of structures and image/patient classification but relevant challenges still remain. As such, the aim of this study is to develop CV algorithms, using Deep Learning techniques, to create tools that can aid doctors in the diagnosis of viral and bacterial pneumonia (VP and BP) through POCUS exams. To do so, convolutional neural networks were designed to perform in classification tasks. The architectures chosen to build these models were the VGG16, ResNet50, DenseNet169 e MobileNetV2. Patients images were divided in three classes: healthy (HE), BP and VP (which includes COVID-19). Through a comparative study, which was based on several performance metrics, the model based on the DenseNet169 architecture was designated as the best performing model, achieving 78% average accuracy value of the five iterations of 5- Fold Cross-Validation. Given that the currently available POCUS datasets for COVID-19 are still limited, the training of the models was negatively affected by such and the models were not tested in an independent dataset. Furthermore, it was also not possible to perform lesion detection tasks. Nonetheless, in order to provide explainability and understanding of the models, Gradient-weighted Class Activation Mapping (GradCAM) were used as a tool to highlight the most relevant classification regions. Clinical relevance - Reveals the potential of POCUS to support COVID-19 screening. The results are very promising although the dataset is limite

Abstract
In this paper we examine the use of social media as a marketing channel by Higher Education Institutions (HEI) and its impact on the institution's brand, attracting top professionals and students. HEIs are annually evaluated globally based on various success parameters to be published in rankings. Specifically, we analyze the Twitter publishing strategies of the selected HEIs, and we compare the results with their overall ranking positions. Our study shows that there are no significant differences between the well-known university rankings based on Kendall t and RBO metrics. However, our data retrieval indicates a tendency for the top-ranked universities to adopt specific strategies, which are further emphasized when analyzing emotions and topics. Conversely, some universities have less prominent strategies that do not align with their ranking positions. This study provides insights into the role of social media in the marketing strategies of HEIs and its impact on their global rankings. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
Energy communities (ECs) are emerging as a promising step to mitigate energy poverty and climate changes, since their main objective is to obtain environmental, economic, and social benefits for the participants, namely in terms of increasing local production using primary renewable resources. In the European Union (EU), Directives D2018 and D944 established a common regime for the promotion of ECs. Given the relevance of the topic, comparing regulations in force in Brazil, Germany, Portugal, and Spain, can contribute to mitigate risks, as well as save time and energy resources. Among the assessed aspects, this work analyzes requirements to access to the activity and measurement issues, which are already well and clearly defined. As for business models and remuneration, focus is given to energy cooperatives and feed-in payments. In turn, the main barriers include financing, end of incentives, need to develop new business models, and issues related to peer-to-peer (P2P) transactions. © 2022 IEEE.

Abstract
Supplier selection has received substantial consideration in the literature since it is considered one of the key levers contributing to a firm's success. Selecting the right suppliers for different product items requires an appropriate problem framing and a suitable approach. Despite the vast literature on this topic, there is not a comprehensive framework underlying the supplier selection process that addresses those concerns. This paper formalizes a framework that provides guidance on how supplier selection should be formulated and approached for different types of items segmented in Kraljic's portfolio matrix and production policies. The framework derives from a thorough literature review, which explores the main dimensions in supplier selection, including sourcing strategy, decision scope and environment, selection criteria, and solution approaches. 326 papers, published from 2000 to 2021, were reviewed for said purpose. The results indicate that supplier selection regarding items with a high purchasing importance should lead to holistic selection criteria. In addition, items comprising a high complexity of supply and production activities should require integrated selection and different sources of uncertainty associated with decision scope and environment, respectively, to solve it, as well as hybrid approaches. There are still many research opportunities in the supplier selection area, particularly in the integrated selection problems and hybrid solution methods, as well as in the risk mitigation, sustainability goals, and new technology adoption.