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
We present new Very Large Telescope Interferometer (VLTI)/GRAVITY near-infrared interferometric measurements of the angular size of the innermost hot dust continuum for 14 type 1 active galactic nuclei (AGNs). The angular sizes are resolved on scales of similar to 0.7 mas and the inferred ring radii range from 0.028 to 1.33 pc, comparable to those reported previously and a factor of 10-20 smaller than the mid-infrared sizes in the literature. Combining our new data with previously published values, we compiled a sample of 25 AGNs with bolometric luminosity ranging from 10(42) to 10(47) erg s(-1), with which we studied the radius-luminosity (R - L) relation for the hot dust structure. Our interferometric measurements of radius are offset by a factor of 2 from the equivalent relation derived through reverberation mapping. Using a simple model to explore the dust structure's geometry, we conclude that this offset can be explained if the 2 mu m emitting surface has a concave shape. Our data show that the slope of the relation is in line with the canonical R proportional to L-0.5 when using an appropriately non-linear correction for bolometric luminosity. In contrast, using optical luminosity or applying a constant bolometric correction to it results in a significant deviation in the slope, suggesting a potential luminosity dependence on the spectral energy distribution. Over four orders of magnitude in luminosity, the intrinsic scatter around the R - L relation is 0.2 dex, suggesting a tight correlation between the innermost hot dust structure size and the AGN luminosity.

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.

Abstract
Diagnosis of health disorders relies heavily on detecting biological data and accurately observing pathological changes. A significant challenge lies in detecting targeted biological signals and developing reliable sensing technology for clinically relevant results. The combination of data analytics with the sensing abilities of Optical Fiber Tweezers (OFT) provides a high-capability, multifunctional biosensing approach for biophotonic tools. In this work, we introduced phase as a new domain to obtain light patterns in OFT back-scattering signals. By applying a multivariate data analysis procedure, we extract phase spectral information for discriminating micro and nano (bio)particles. A newly proposed method-Hilbert Phase Slope-presented high suitability for differentiation problems, providing features able to discriminate with statistical significance two optically trapped human tumoral cells (MKN45 gastric cell line) and two classes of non-trapped cancer-derived extracellular nanovesicles - an important outcome in view of the current challenges of label-free bio-detection for multifunctional single-molecule analytic tools.