Abstract
Although the classification of chest radiographs has long been an extensively researched topic, interest increased significantly with the onset of the COVID-19 pandemic. Existing results are promising; however, the radiological similarities between COVID-19 and other types of respiratory diseases limit the success of conventional image classification approaches that focus on single instances. This study proposes a novel perspective that conceptualizes COVID-19 pneumonia as a deviation from a normative distribution of typical pneumonia patterns. Using a population- based approach, our approach utilizes distributional anomaly detection. This method diverges from traditional instance-wise approaches by focusing on sets of scans instead of individual images. Using an autoencoder to extract feature representations, we present instance-based and distribution-based assessments of the separability between COVID-positive and COVIDnegative pneumonia radiographs. The results demonstrate that the proposed distribution-based methodology outperforms conventional instance-based techniques in identifying radiographic changes associated with COVID-positive cases. This underscores its potential as an early warning system capable of detecting significant distributional shifts in radiographic data. By continuously monitoring these changes, this approach offers a mechanism for early identification of emerging health trends, potentially signaling the onset of new pandemics and enabling prompt public health responses.

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Abstract
Purpose This study aims to explore worldwide innovative solutions that have been proposed to mitigate the effects of COVID-19 pandemic on people's mental health. Design/methodology/approach A qualitative methodology is adopted, which performs an exploratory study considering the innovative projects identified by the Observatory for Public Sector Innovation framework. Additionally, the analysis of the relevance and characteristics of these projects are explored considering a multidimensional framework composed of five dimensions: novelty level; social need; improvement of society; sector neutrality; and level of emergence. Findings The findings reveal that the number of projects in the field of mental health is low, despite their strong relevance to their communities. These projects arise from a strong social need to protect especially the most vulnerable groups in this pandemic and involve a large number of partners in the public sector, business and civil society. The role of volunteering in the revitalization and growth of these initiatives is also recognized. Originality/value This study is relevant in both the theoretical and practical dimensions. It allows the exploration of these projects considering the dimensions of social innovation and offers practical implications that allow these projects to be replicated in other countries and regions.

Abstract
Future electrical grids, particularly the distribution networks, may face more severe voltage rises/drops, and in general, more power quality problems in the presence of new loads such as electric vehicle chargers and renewable energy generation units like photovoltaic systems. This necessitates investing in additional high-cost infrastructure to increase the capability of the feeder in hosting higher levels of loads and generation units while the existing capacity is not utilized effectively. In the stated condition, effective voltage stabilization strategies in electrical distribution networks can contribute to hosting capacity improvement and the better utilization of the existing infrastructure. Accordingly, in this paper, the application of Open-UPQC in voltage profile improvement and hosting capacity enhancement is evaluated in low-voltage distribution networks. Furthermore, a dynamic reference voltage adjustment strategy is applied to the device to improve its capabilities in power quality improvement and hosting capacity enhancement. Simulation studies have been implemented to evaluate the capability of Open-UPQC either with static reference voltage or the dynamically-adjusted one in low-voltage networks with real measured data while different cases are assessed regarding the topology and the length of the feeder. The simulation results approved the capability of Open-UPQC especially with the dynamic reference voltage in hosting capacity enhancement while providing the highest level of voltage profile improvement among all the assessed custom power devices in the studied low-voltage networks.

Abstract
PurposeRefugee entrepreneurship is increasingly viewed as a silver bullet being able to promote host countries' economic performance and enable the successful integration of refugees. This study aims to identify the main determinants of entrepreneurial intentions of refugees in Portugal based on the underdog theory.Design/methodology/approachIn this study, the authors scrutinize the entrepreneurial intentions of refugees living in Portugal, an overlooked context, using a purpose-built inquiry responded to by 41 refugees and resorting to fuzzy-set qualitative comparative analysis, complemented with partial least squares path modeling.FindingsSome important results are worth highlighting: the entrepreneurial intentions of the respondent sample of refugees living in Portugal are high; the theoretical arguments underlying the underdog or challenge-based entrepreneurship theory are validated in the context of the respondent sample; and psychological related factors associated with the more standard explanations of entrepreneurial intentions constitute necessary conditions for high refugee entrepreneurial intentions.Originality/valueEntrepreneurial intentions to launch a business have been discussed in the entrepreneurship literature vastly, but it has not yet received much attention when focusing on refugees, often identified as underdogs (potential) entrepreneurs. This study contributes to the literature by testing the challenge-based entrepreneurship theory to identify the primary factors influencing refugee entrepreneurial intentions.

Abstract
Software performance concerns have been attracting research interest at an increasing rate, especially regarding energy performance in non-wired computing devices. In the context of mobile devices, several research works have been devoted to assessing the performance of software and its underlying code. One important contribution of such research efforts is sets of programming guidelines aiming at identifying efficient and inefficient programming practices, and consequently to steer software developers to write performance-friendly code.Despite recent efforts in this direction, it is still almost unfeasible to obtain universal and up-to-date knowledge regarding software and respective source code performance. Namely regarding energy performance, where there has been growing interest in optimizing software energy consumption due to the power restrictions of such devices. There are still many difficulties reported by the community in measuring performance, namely in large-scale validation and replication. The Android ecosystem is a particular example, where the great fragmentation of the platform, the constant evolution of the hardware, the software platform, the development libraries themselves, and the fact that most of the platform tools are integrated into the IDE's GUI, makes it extremely difficult to perform performance studies based on large sets of data/applications. In this paper, we analyze the execution of a diversified corpus of applications of significant magnitude. We analyze the source-code performance of 1322 versions of 215 different Android applications, dynamically executed with over than 27900 tested scenarios, using state-of-the-art black-box testing frameworks with different combinations of GUI inputs. Our empirical analysis allowed to observe that semantic program changes such as adding functionality and repairing bugfixes are the changes more associated with relevant impact on energy performance. Furthermore, we also demonstrate that several coding practices previously identified as energy-greedy do not replicate such behavior in our execution context and can have distinct impacts across several performance indicators: runtime, memory and energy consumption. Some of these practices include some performance issues reported by the Android Lint and Android SDK APIs. We also provide evidence that the evaluated performance indicators have little to no correlation with the performance issues' priority detected by Android Lint. Finally, our results allowed us to demonstrate that there are significant differences in terms of performance between the most used libraries suited for implementing common programming tasks, such as HTTP communication, JSON manipulation, image loading/rendering, among others, providing a set of recommendations to select the most efficient library for each performance indicator. Based on the conclusions drawn and in the extension of the developed work, we also synthesized a set of guidelines that can be used by practitioners to replicate energy studies and build more efficient mobile software.