Abstract
This paper presents a matheuristic solution algorithm to solve the well-known resource-constrained project scheduling problem (RCPSP). The problem makes use of a restricted neighbourhood method using an activity selection and a search space restriction module and implements them as two alternative search algorithms. The first algorithm makes use of the best-performing components of the branch-and-bound procedures from the literature, and embeds them into a greedy neighbourhood search. The second matheuristic implements the exact branch-and-bound procedures into a known and well-performing meta-heuristic search algorithm. Computational experiments have been carried out on seven different datasets consisting of 10,000+ project instances. Experiments reveal that the choice of exact algorithm is key in finding high-quality solutions, and illustrate that the trade-off between selecting an activity set size and search space restriction depends on the specific implementation. The computational tests demonstrate that the matheuristic discovered 24 new best known solutions that could not be found by either a meta-heuristic or an exact method individually. Moreover, a new benchmark dataset has been proposed that can be used to develop new matheuristic search procedures to solve the problem consisting of 461 instances from the literature.

Abstract
Luxembourg, known for its immigration history, attracts immigrants to work. This study analyses different immigrant groups in the labour market from 2014 to 2022 by using Labor Force Survey (LFS) data, Symbolic Data Analysis (SDA), and the Monitoring the Evolution of Clusters (MEC) framework.Based on the birthplace and length of residence in Luxembourg, in each year, microdata were aggregated into 21 symbolic objects. They were primarily described by 16 modal variables which are multi-valued variables with a frequency attached to each category. Moreover, clustering using complete linkage and the Chernoff's distance was applied. The Heuristic Identification of Noisy Variables (HINoV) suggested that with just six variables, objects may be grouped homogeneously. The MEC framework traced temporal relations and transitions between the clusters, revealing some movements across the different years.Results indicate that people from the European Union (EU) and Neighbouring countries have similar profiles while the Portuguese have opposite characteristics. The Luxembourgers are somewhere in between. Profiling people from non-EU countries was challenging.The data and methodology used make it easy to replicate the work in other nations, enabling comparison of results and monitoring to continue in the future.

Abstract
The teaching and learning process in higher education needs continuous cultivation of pedagogical expertise, encompassing subject mastery and pedagogical methodologies. This article explores the transformation of higher education institutions (HEIs) into hybrid campuses and the importance of pedagogical innovation, highlighting the need for training in hybrid/e-learning environments, and emphasizing the potential of mobile technologies. Furthermore, it presents a case study on two professional development courses offered to faculty members, working in the field of Engineering in Portugal, aiming to reconfigure their professionality. The research adopts an ethnographic methodology, integrating quantitative methods and utilizing a variety of data collection tools, including field notes and self-reflection sheets, to analyze the teachers' reconfiguration of their professional practices. The main findings of the study reveal that the majority of faculty members reported significant gains in transforming traditional courses to digital formats, mastering various online platforms and tools, and developing skills in online communication.

Abstract
Rising adoption of AI-driven solutions in medical imaging is associated with an emerging need to develop strategies to introduce explainability as an important aspect of trustworthiness of AI models. This chapter addresses the most commonly used explainability techniques in medical image analysis, namely methods generating visual, example-based, textual, and concept-based explanations. To obtain visual explanations, we explore backpropagation- and perturbation-based methods. To yield example-based explanations, we focus on prototype-, distance-, and retrieval-based techniques, as well as counterfactual explanations. Finally, to produce textual and concept-based explanations, we delve into image captioning and testing with concept activation vectors, respectively. This chapter aims at providing understanding of the conceptual underpinning, advantages and limitations of each method, as well as to interpret their generated explanations in the context of medical image analysis.

Abstract
This theoretical study presents a D-shaped photonic crystal fiber (PCF) surface plasmon resonance (SPR) based sensor designed for humidity detection in transformer oil. Humidity refers to the presence of water dissolved or suspended in the oil, which can affect its dielectric properties and, consequently, the efficiency and safety of the transformer's operation, failures in the sealing system and the phenomenon of condensation can be the main sources of this humidity. This sensor leverages the unique properties of the coupling between surface plasmons and fiber guided mode at the Au-PCF interface to enhance the sensitivity to humidity changes in the external environment. The research demonstrated the sensor's efficacy in monitoring humidity levels ranging from 0% to 100% with an average sensitivity of measured at 1106.1 nm/RIU. This high sensitivity indicates a substantial shift in the resonance wavelength corresponding to minor changes in the refractive index caused by varying humidity levels, which is critically important in the context of transformer maintenance and safety. Transformer oil serves as both an insulator and a coolant, and its humidity level is a key parameter influencing the performance and longevity of transformers. Excessive humidity can lead to insulation failure and reduced efficiency and, therefore, the ability to accurately detect and monitor humidity levels in transformer oil can significantly enhance preventive maintenance strategies, reduce downtime, and prevent potential failures, ensuring the reliable operation of electrical power systems. © The Authors.

Abstract
As we delve into how technology enhances supply chain management efficiency and tackles specific e-business challenges, we must recognize the critical synergy with recommendation systems. These systems align with digital transformation goals, enhancing customer experiences, enabling data-driven decisions, promoting innovation, and embracing a customer-centric approach. During the 2020 COVID-19 surge, e-commerce experienced increased activity, highlighting the significance of recommendation systems in forecasting new purchases. This chapter introduces a novel approach to understanding customer–product interactions through multilayer bipartite networks, employing a hybrid recommendation system with k-means and weighted slope one algorithms. This approach enhances clarity, explainability, and information gains, aiding tasks like inventory optimization. The study concludes that the model’s predicted results differ from the actual ratings and that the system is effective in improving decision-making processes and customer recommendations. © 2025 selection and editorial matter, Adelaide Martins and Carolina Machado.