Abstract

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
Microgrids are poised to become the building blocks of the future control architecture of electric power systems. As the number of controllable points in the system grows exponentially, traditional control and optimization algorithms become inappropriate for the required operation time frameworks. Reinforcement learning has emerged as a potential alternative to carry out the real-time dispatching of distributed energy resources. This paper applies one of the continuous action-space algorithms, proximal policy optimization, to the optimal dispatch of a battery in a grid-connected microgrid. Our simulations show that, though suboptimal, RL presents some advantages over traditional optimization setups. Firstly, it can avoid the use of forecast data and presents a lower computational burden, therefore allowing for implementation in distributed control devices.

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Background Engaging in advance care planning can be emotionally challenging, but gamification and technology are suggested as a potential solution.Objective Present the development stages of a mobile app prototype to improve quality of life for patients in palliative care.Design The study started with a comprehensive literature review to establish a foundation. Subsequently, interviews were conducted to validate the proposed features of the mobile application. Following the development phase, usability tests were conducted to evaluate the overall usability of the mobile application. Furthermore, an oral questionnaire was administered to understand user satisfaction about the implemented features.Results A three-phase testing approach was employed based on the chosen user-centred design methodology to obtain the results. Three iterations were conducted, with improvements being made based on feedback and tested in subsequent phases. Despite the added complexity arising from the health status of patients in palliative care, the usability tests and implemented features received positive feedback from both patients and healthcare providers.Conclusion The research findings have demonstrated the potential of digitisation in enhancing the quality of life for patients in palliative care. This was achieved through the implementation of patient-centred design, personalised care, the inclusion of social chatrooms and facilitating end-of-life discussions.

Abstract
Influential spreaders are key nodes in networks that maximize or control the spreading processes. Many real-world systems are represented as weighted networks, and several indexing methods, such as weighted betweenness, closeness, k-shell decomposition, voterank, and mixed degree decomposition, among others, have been proposed to identify these influential nodes. However, these methods often face limitations such as high computational cost, non-monotonic rankings, and reliance on tunable parameters. To address these issues, this paper introduces a new tunable parameter-free method, Normalized Strength-Degree Centrality (nsd), which efficiently combines a node's normalized degree and strength to measure its influence across various network structures. Experimental results on eleven real and synthetic weighted networks show that nsd outperforms the existing methods in accurately identifying influential spreaders, strongly correlating to the Weighted Susceptible-Infected-Recovered (WSIR) model. Additionally, nsd is a parameter-free method that does not require time-consuming preprocessing to estimate rankings.

Abstract
The efficient management of assets delivers value and is essential for achieving service objectives, managing risks, and reducing costs. This paper proposes decision-support methods to help capital-intensive industries manage their assets and optimise their life cycle. Optimisation approaches were developed to support longterm investment planning by maximising the value created and minimising the budget used. Also, the trade-off for both objectives was analysed. Using the proposed models will lead to efficient management of available capital and excellent service delivery. Thus, water companies will fulfil the regulator's requirements and present well-founded decision-making. This study was applied to a Portuguese water utility.