Abstract
Optical trapping provides a way to isolate, manipulate, and probe a wide range of microscopic particles. Moreover, as particle dynamics are strongly affected by their shape and composition, optical tweezers can also be used to identify and classify particles, paving the way for multiple applications such as intelligent microfluidic devices for personalized medicine purposes, or integrated sensing for bioengineering. In this work, we explore the possibility of using properties of the forward scattered radiation of the optical trapping beam to analyze properties of the trapped specimen and deploy an autonomous classification algorithm. For this purpose, we process the signal in the Fourier domain and apply a dimensionality reduction technique using UMAP algorithms, before using the reduced number of features to feed standard machine learning algorithms such as K-nearest neighbors or random forests. Using a stratified 5-fold cross-validation procedure, our results show that the implemented classification strategy allows the identification of particle material with accuracies up to 80%, demonstrating the potential of using signal processing techniques to probe properties of optical trapped particles based on the forward scattered light. Furthermore, preliminary results of an autonomous implementation in a standard experimental optical tweezers setup show similar differentiation capabilities for real-time applications, thus opening some opportunities towards technological applications such as intelligent microfluidic devices and solutions for biochemical and biophysical sensing. © Published under licence by IOP Publishing Ltd.

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