Abstract
Similar to software systems, programming languages evolve substantially over time. Indeed, the community has more recently seen the release of new versions of mainstream languages in shorter and shorter time frames. For instance, the C++ working group has begun to release a new version of the language every 3 years, which now has a greater number of modern C++ features and improvements in modern standards (C++11, C++14, C++17, and C++ 20). Nonetheless, there is little empirical evidence on how developers are transitioning to use modern C++ constructs in legacy systems, and not understanding the trends and reasons for adopting these new modern C++ features might hinder software developers in conducting rejuvenation efforts. In this paper, we conduct an in-depth study to understand the development practices of KDE contributors to evolve their projects toward the use of modern C++ features. Our results show a trend in the widespread adoption of some modern C++ features (lambda expressions, auto-typed variables, and range-based for) in KDE community projects. We also found that developers in the KDE community are making large efforts to modernize their programs using automated tools, and we present some modernization scenarios and the benefits of adopting modern C++ features of the C++ programming language. Our results might help C++ software developers, in general, to evolve C++ legacy systems and tools builders to implement more effective tools that could help in rejuvenation efforts.

Abstract
Previous studies show that digital transition brings several benefits and challenges for companies. Among those challenges, particularly for Small and Medium-sized Enterprises (SMEs), the main one is increased capacitation, from technical roles to management. Considering this, the main objective of this study is to identify the training needs and the ecosystem support in the face of the digital transition for Portuguese manufacturing SMEs.Semi-structured interviews were conducted with industry experts and company professionals in the automotive and textile sectors. It was concluded that all workers, from technical roles to middle and top management, need more digital capabilities and would benefit from training programmes. The most desired areas for training are data science, virtualisation skills, quality assurance, technical training, and soft skills. The preferred format is physical (or hybrid at most) during working hours and with theoretical training before on-the-job learning. Both industrial companies and experts believe in the value of involving external entities in the training of employees, with the three most referred entities being technology and interface centres, universities, and business associations.

Abstract
This chapter presents key insights for the planning and operation of distribution power grids integrating high shares of renewable generation and charging capacity for electric vehicles (EVs). Case studies are presented to illustrate the impact of expected trends for vehicle electrification in the operation and future expansion of distribution power grids. The potential of innovative approaches is also exploited. The smart-transformer concept based on solid-state-transformer architectures as well as hybrid AC/DC distribution grids is qualitatively evaluated as a suitable solution for the massive integration of EV charging.

Abstract
The adoption of residential photovoltaic (PV) systems to mitigate the effects of climate change has been incentivized in recent years by government policies. Due to the impacts of these systems on the energy mix and the electrical grid, it is essential to understand how these technologies will expand in urban areas. To fulfill that need, this article presents an innovative method for modeling the diffusion of residential PV systems in urban environments that employs spatial analysis and urban characteristics to identify residences at the subarea level with the potential for installing PV systems, along with temporal analysis to project the adoption growth of these systems over time. This approach integrates urban characteristics such as population density, socioeconomic data, public environmental awareness, rooftop space availability, and population interest in new technologies. Results for the diffusion of PV systems in a Brazilian city are compared with real adoption data. The results are presented in thematic maps showing the spatiotemporal distribution of potential adopters of PV systems. This information is essential for creating efficient decarbonization plans because, while many households can afford these systems, interest in new technologies and knowledge of the benefits of clean energy are also necessary for their adoption.

Abstract
Explainable Artificial Intelligence (XAI) is an area of growing interest, particularly in medical imaging, where example-based techniques show great potential. This paper is a systematic review of recent example-based XAI techniques, a promising approach that remains relatively unexplored in clinical practice and medical image analysis. A selection and analysis of recent studies using example-based XAI techniques for interpreting medical images was carried out. Several approaches were examined, highlighting how each contributes to increasing accuracy, transparency, and usability in medical applications. These techniques were compared and discussed in detail, considering their advantages and limitations in the context of medical imaging, with a focus on improving the integration of these technologies into clinical practice and medical decision-making. The review also pointed out gaps in current research, suggesting directions for future investigations. The need to develop XAI methods that are not only technically efficient but also ethically responsible and adaptable to the needs of healthcare professionals was emphasised. Thus, the paper sought to establish a solid foundation for understanding and advancing example-based XAI techniques in medical imaging, promoting a more integrated and patient-centred approach to medicine.

Abstract
Glaucoma is one of the leading causes of irreversible blindness in the world. Early diagnosis and treatment increase the chances of preserving vision. However, despite advances in techniques for the functional and structural assessment of the retina, specialists still encounter many challenges, in part due to the different presentations of the standard optic nerve head (ONH) in the population, the lack of explicit references that define the limits of glaucomatous optic neuropathy (GON), specialist experience, and the quality of patients' responses to some ancillary exams. Computer vision uses deep learning (DL) methodologies, successfully applied to assist in the diagnosis and progression of GON, with the potential to provide objective references for classification, avoiding possible biases in experts' decisions. To this end, studies have used color fundus photographs (CFPs), functional exams such as visual field (VF), and structural exams such as optical coherence tomography (OCT). However, it is still necessary to know the minimum limits of detection of GON characteristics performed through these methodologies. This study analyzes the use of deep learning (DL) methodologies in the various stages of glaucoma screening compared to the clinic to reduce the costs of GON assessment and the work carried out by specialists, to improve the speed of diagnosis, and to homogenize opinions. It concludes that the DL methodologies used in automated glaucoma screening can bring more robust results closer to reality.