Abstract
Data description is a fundamental step in Research Data Management (RDM). When it comes to images, the challenge is increased, as they have characteristics that differentiate them from other typologies. We conducted a study in which we obtained a set of 27 images described according to their content, by researchers of the projects where they are inserted. After obtaining the ground-truth that would support the analysis, we proceeded to two more stages of description, one through an automatic processing tool (Vision AI) and the other through researchers with no knowledge of the images. We concluded that the human description is more elucidative of the images' content, namely at a semantic level. In turn, the automatic tools enhance a more literal description. This study allowed us to reflect on the description of images in a research context and to discuss the potential of formal analysis and analysis of the semantic expression of images.

Abstract
Research data management includes activities that organize and manage the life of a research project and is crucial for consistent work performance. Some activities are related to the description, which is a fundamental step, since it allows data to be properly documented and interpreted, promoting their subsequent reuse and sharing. The description is usually done through text, but other typologies can also be used, such as images, taking advantage of their potential and particular characteristics to promote description. We used a qualitative method of investigation through an exploratory case study. We conducted 16 semi-structured interviews, with researchers who have produced, described, and published research data, in order to understand how images can assume the role of metadata in data description. We found that all interviewees would like to have the possibility of describing data with images, but they consider that the publishing platforms have to be prepared for this. Most researchers were able to identify descriptors that could include images and also describe those that they consider being the greatest advantages of the project. All researchers consider that images as metadata would be a more direct gateway to the data. The issue of data description through resources other than text has never been properly investigated. The existing literature does not develop the theme, although images have had an abrupt growth in society and science. This work aims to open new paths, raise new ideas and raise awareness of new and original practices.

Abstract
Fake news spreads rapidly, creating issues and making detection harder. The purpose of this study is to determine if fake news contains sentiment polarity (positive or negative), identify the polarity of sentiment present in their textual content and determine whether sentiment polarity is a reliable indication of fake news. For this, we use a deep learning model called BERT (Bidirectional Encoder Representations from Transformers), trained on a sentiment polarity dataset to classify the polarity of sentiments from a dataset of true and fake news. The findings show that sentiment polarity is not a reliable single feature for recognizing false news correctly and must be combined with other parameters to improve classification accuracy. © 2024 The Author(s). Published by Elsevier B.V.

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 paper present an instance transformation procedure to modify known instances of the resource -constrained project scheduling problem to make them easier to solve by heuristic and/or exact solution algorithms. The procedure makes use of a set of transformation rules that aim at reducing the feasible search space without excluding at least one possible optimal solution. The procedure will be applied to a set of 11,183 instances and it will be shown by a set of experiments that these transformations lead to 110 improved lower bounds, 16 new and better schedules (found by three meta -heuristic procedures and a set of branch -and -bound procedures) and even 64 new optimal solutions which were never not found before.

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.