Abstract

Abstract
Testing software is very important, but not done well, resulting in problematic and erroneous software applications. The cause radicates from a skills mismatch between what is needed in industry, the learning needs of students, and the way testing is currently being taught at higher and vocational education institutes. The goal of this project is to identify and design seamless teaching materials for testing that are aligned with industry and learning needs. To represent the entire socio-economic environment that will benefit from the results, this project consortium is composed of a diverse set of partners ranging from universities to small enterprises. The project starts with research in sensemaking and cognitive models when doing and learning testing. Moreover, a study will be done to identify the needs of industry for training and knowledge transfer processes for testing. Based on the outcomes of this research and the study, we will design and develop capsules on teaching software testing including the instructional materials that take into account the cognitive models of students and the industry needs. Finally, we will validate these teaching testing capsules developed during the project.

Abstract
Building renewable energy communities (REC) involves investments on generation facilities (such as PV panels), technologies to provide flexibility (such as batteries), management platforms and ICT systems, as well as integrating other flexibility sources such as thermal storage or electric vehicles. The way investments are made by the REC's members and other third parties is in close relationship with the governance models of the REC in terms of energy, flexibility and costs and benefits sharing, which, in the end, constitute the overall REC's business model. This works provides a revision of the main financing mechanisms to invest on and build a REC, and of the associated governance and business models that result from the investments mechanisms selected and its implications on its day by day operation. © 2022 IEEE.

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.