Abstract
Machine Learning models need large amounts of annotated data for training. In the field of medical imaging, labeled data is especially difficult to obtain because the annotations have to be performed by qualified physicians. Natural Language Processing (NLP) tools can be applied to radiology reports to extract labels for medical images automatically. Compared to manual labeling, this approach requires smaller annotation efforts and can therefore facilitate the creation of labeled medical image data sets. In this article, we summarize the literature on this topic spanning from 2013 to 2023, starting with a meta-analysis of the included articles, followed by a qualitative and quantitative systematization of the results. Overall, we found four types of studies on the extraction of labels from radiology reports: those describing systems based on symbolic NLP, statistical NLP, neural NLP, and those describing systems combining or comparing two or more of the latter. Despite the large variety of existing approaches, there is still room for further improvement. This work can contribute to the development of new techniques or the improvement of existing ones.

Abstract
More and more people are relying on the Web to find health information. Challenges faced by individuals with low health literacy in the real world likely persist in the virtual realm. To assist these users, our first step is to identify them. This study aims to uncover disparities in the information-seeking behavior of users with varying levels of health literacy. We utilized data gathered from a prior user experiment. Our approach involves a classification scheme encompassing events during web search sessions, spanning the browser, search engine, and web pages. Employing this scheme, we logged interactions from video recordings in the user study and subjected the event logs to descriptive and inferential analyses. Our data analysis unveils distinctive patterns within the low health literacy group. They exhibit a higher frequency of query reformulations with entirely new terms, engage in more left clicks, utilize the browser's backward functionality more frequently, and invest more time in interactions, including increased scrolling on results pages. Conversely, the high health literacy group demonstrates a greater propensity to click on universal results, extract text from URLs more often, and make more clicks with the mouse middle button. These findings offer valuable insights for inferring users' health literacy in a non-intrusive manner. The automatic inference of health literacy can pave the way for personalized services, enhancing accessibility to information and education for individuals with low health literacy, among other benefits.

Abstract
Various sectors within the heritage domain have developed linked data models to describe their cultural artefacts comprehensively. Within the archival domain, ArchOnto, a data model rooted in CIDOC CRM, uses linked data to open archival information to new uses through the prism of linked data. This paper seeks to investigate the potential to use information in archival records in a larger context. It aims to leverage classes and properties sourced from repositories deemed informal due to their crowd-sourcing nature and the possibility of inconsistencies or lack of precision in the data but rich in content, such as the cases of Wikidata and DBpedia. The anticipated outcome is attaining a more comprehensive and expressive archival description, fostering enhanced understanding and assimilation of archival information among domain specialists and lay users. To achieve this, we first analyse existing archive records currently described under the ISAD(G) standard to discern the typologies of entities involved. Subsequently, we map these entities within the ArchOnto ontology and establish correspondences with alternative models. We observed that entities associated with people, places, and events benefited the most from integrating properties sourced from Wikidata and DBpedia. This integration enhanced their comprehensibility and enriched them at a semantic level. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
This study was initiated at a time of unprecedented uncertainty, as lecturers and educational institutions across the world tried to manage the move to online education as a result of the global COVID-19 pandemic. It started with lecturers' perspectives of their performance during that time to identify innovative teaching strategies beyond the priority of emergency teaching. The main goal was to identify the occurrence of more permanent changes in Higher Education after the pandemic. The research was based on a qualitative approach where faculty members were interviewed about their activities before, during and after lockdown periods. Data collected was analysed with the help of an algorithm based on Artificial Intelligence. Ultimately, it was possible to gather and evaluate practical solutions related to hybrid learning in Europe, Australia, and New Zealand, leading to recommendations for stakeholders in Higher Education.

Abstract
This study evaluates a new solution approach for the Resource -Constrained Project Scheduling with Alternative Subgraphs (RCPSP-AS) in case that complex relations (i.e. nested and linked alternatives) are considered. In the RCPSP-AS, the project activity structure is extended with alternative activity sequences. This implies that only a subset of all activities should be scheduled, which corresponds with a set of activities in the project network that model an alternative execution mode for a work package. Since only the selected activities should be scheduled, the RCPSP-AS comes down to a traditional RCPSP problem when the selection subproblem is solved. It is known that the RCPSP and, hence, its extension to the RCPSP-AS is NP -hard. Since similar scheduling and selection subproblems have already been successfully solved by satisfiability (SAT) solvers in the existing literature, we aim to test the performance of a GA -SAT approach that is derived from the literature and adjusted to be able to deal with the problem -specific constraints of the RCPSP-AS. Computational results on smalland large-scale instances (both artificial and empirical) show that the algorithm can compete with existing metaheuristic algorithms from the literature. Also, the performance is compared with an exact mathematical solver and learning behaviour is observed and analysed. This research again validates the broad applicability of SAT solvers as well as the need to search for better and more suited algorithms for the RCPSP-AS and its extensions.

Abstract
The research objective of this work is to develop and evaluate an enterprise architecture for rural accommodation in the Iberian Peninsula that responds to the demand of the remote labor market. Through an extensive literature review and the application of ArchiMate modeling, this study focuses on providing an enterprise architecture that promotes business resilience and environmental sustainability and boosts the local economy. The proposed enterprise architecture is remotely evaluated by experts, highlighting potential benefits, challenges, and areas for improvement. The results show that the proposed enterprise architecture has the potential to improve the long-term success of rural lodging businesses, enhance the customer experience, promote sustainability, and contribute to economic growth in rural areas through value exchange among stakeholders. The ArchiMate model provides a holistic perspective on stakeholder interactions and interoperability across all functional business areas: Customer Service, Product Management, Omnichannel Commerce, Human Resources, Business Strategy, Marketing, and Sustainability Management. The idea is to empower rural lodging businesses to create a better customer experience, achieve energy and environmental efficiency, contribute to local development, respond quickly to regulatory changes and compliance, and develop new revenue streams. The main goal is to improve offers, mitigate seasonal effects, and reverse the continuous cycle of decline in areas with low population density. Therefore, this ArchiMate modeling can be the initial basis for the digitization or expansion of the rural lodging industry in other geographies.