Abstract
The large-scale integration of electric vehicles (EVs) can contribute to the better use of renewable resources and the emergence of new technologies. However, if not properly controlled, it has several downsides. Several strategies make it possible to perform this control by making use of data mining models to deal with the large amounts of data associated with EVs that need to be considered. Accordingly, this chapter presents a study on the progress of EVs integration, where the economic and socio-demographic aspects and the development of the EVs global market are highlighted. Furthermore, some recommendations are suggested to policymakers related to EV management and possibilities for future improvement of EV integration. Finally, this chapter provides a review of data mining models and applications that deal, directly or indirectly, with EV-related problems. © 2023 The Institute of Electrical and Electronics Engineers, Inc.

Abstract
Introduction and objectives: [ 99mTc] Tc-HMPAO, developed for brain imaging, is taken up by the heart, but never used to study it. We aimed to compare cardiac images with [99mTc] Tc-HMPAO and [ 99mTc] Tc-Tetrofosmin, using imaging techniques. Materials and methods: Cardiac gated SPECTs with [99mTc] Tc-HMPAO were compared with myocardial perfusion scintigraphies (MPS) with [99mTc] Tc-Tetrofosmin in three inpatients, from the vascular surgery ward. We developed algorithms in MATLAB R2016 to compare the [99mTc] Tc-Tetrofosmin/ [99mTc] Tc-HMPAO images. Pixel-wise correlations for slices, reversibility, and polar maps were obtained. Results: Correlations of both radiotracers’ myocardial images were as high as 0.93. Polar maps correlations were 0.93-0.95 (for both stress and rest) and 0.62-0.90 (reversibility). One of the patients (smoker) had significant lung [99mTc] Tc-HMPAO uptake. Conclusions: Cardiac SPECT with [99mTc] Tc-HMPAO might be a screening method for myocardial ischemia in non-smoking patients with epilepsy suspected of having heart changes, and who need to perform a brain perfusion SPECT. Keywords: myocardial perfusion scintigraphy; epilepsy; HMPAO; myocardial SPECT.

Abstract
Recent economic and public health crises have posed important challenges to family businesses - particularly those in the hospitality sector. While sustaining a business, performance becomes critical; there is insufficient knowledge on the use of entrepreneurial behaviors in mitigating the impact of a crisis by family businesses. To help fill this gap, this study explores the configurations of entrepreneurial behaviors that lead to improved performance in small firms under crisis market conditions - particularly, risk-taking, innovativeness, proactiveness, flexibility, and digitalization. This study employs fuzzy-set qualitative comparative analysis (fsQCA). The sample consists of 117 one- and two-star Polish hotels that are comprised of both family and non-family businesses. The data was collected in November and December 2021. The results confirm the core role of risk-taking, proactiveness, and flexibility in increasing the performance of these small firms. However, performance outcomes depend on the configurations of the firms; differences between family and non-family businesses stood out. In family hotels, risk-taking is accompanied by flexibility as a core factor, and digitalization does not play an important role in achieving higher performance. Overall, these results contribute to the literature on organizational entrepreneurship (especially entrepreneurial orientation) as well as family business crisis management in the tourism sector. These findings offer implications for managers by indicating combinations of entrepreneurial behaviors that can help foster business performance.

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Abstract
Smart systems increasingly demand the processing of a massive amount of data generated by heterogeneous and distributed data sources. Due to the inherent cyber-physical nature of these systems, many applications require that this processing respects a set of non-functional requirements (such as timeliness, or energy-efficiency). To cope with this challenge, edge-cloud architectures need to provide flexible mechanisms to support varying processing needs, whilst guaranteeing the minimum level of quality of service required by these smart applications. This paper addresses this challenge in the context of the ELASTIC software architecture, which has been developed integrating responsive data-in-motion (edge computing) and latent data-at-rest analytics (cloud computing) into a single solution, satisfying extreme-scale analytics' performance requirements. The paper focuses on how the architecture fulfils the non-functional properties inherited from the applications, namely real-time and energy-efficiency, whilst ensuring the performance of the software architecture. © 2022 IEEE.