Abstract
Purpose - Customer-provider relationships unfold through multiple touchpoints across different channels. However, some touchpoints are more important than others. Such important touchpoints are viewed as moments of truth (MOTs). This study examines the impact of a series of touchpoints on an MOT, and the role MOTs play in determining future profitability and other behavioral outcomes (e.g. customer retention and customer cross-buy) in a business-to-business (B2B) context. Design/methodology/approach - Building upon social exchange theory, a conceptual model is proposed and tested that examines the impact of human, digital, and physical touchpoints and past MOTs on customer evaluation of a current MOT and on future customer outcomes. This research employs a longitudinal methodology based on a unique panel dataset of 2,970 B2B customers. Findings - Study results show that all touchpoints significantly contribute to MOTs, while human and physical touchpoints maintain their primacy during MOTs. The impact of MOTs on future customer outcomes is also demonstrated. Practical implications - This study highlights the need for prioritizing human and physical touchpoints in managing MOTs, and for carefully managing MOTs across time. Originality/value - Given its B2B outlook and longitudinal approach, this research contributes to the multichannel and interactive marketing literature by determining relevant touchpoints for B2B customers.

Abstract
As organizations increasingly transition from monolithic systems to microservices, they aim to achieve higher availability, automatic scaling, simplified infrastructure management, enhanced collaboration, and streamlined deployments. However, this migration process remains largely manual and labour-intensive. While existing literature offers various strategies for decomposing monoliths, these approaches primarily focus on architecture-level guidance, often overlooking the code-level challenges and dependencies that developers must address during the migration. This article introduces a catalogue of seven refactorings specifically designed to support the transition to a microservices architecture with a focus on handling dependencies. The catalogue provides developers with a systematic guide that consolidates refactorings identified in the literature and addresses the critical gap in systematizing the process at the code level. By offering a structured, step-by-step approach, this work simplifies the migration process and lays the groundwork for its potential automation, empowering developers to implement these changes efficiently and effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Background: Virtual Reality (VR) is making education more engaging and accessible, especially for students with Autism Spectrum Disorders (ASD), promoting inclusion and the development of STEM skills in innovative ways. The literature still reveals a significant gap in terms of appropriate educational resources adapted to the specific needs of these students, resulting in difficulties in their inclusion. With the growing need for inclusive approaches in education, it is essential to find solutions to support these students. The aim of this study is to validate the data collection methodology that will enable the development of Virtual Learning Environments with STEM content for students with ASD. Methods: The Design Science Research (DSR) methodology was used to develop a VR artefact for students with ASD. In addition, the Delphi method was applied in the expert involvement phase, which will contribute to the validation of the artefact's specific requirements. Both will allow for an inclusive and distinctive approach to the development of an artefact, with the aim of offering an innovative educational experience, meeting the varied needs and learning styles of students with ASD, optimising the effectiveness of the proposed VLE. Results: The results show a strong acceptance among experts, highlighting the potential positive impact of this approach, although there are aspects to be improved to ensure a more comprehensive and effective approach. Conclusions: This study highlights the successful validation of an innovative virtual reality programme for students with ASD, highlighting the importance of interdisciplinary collaboration and the strong contribution to the advancement of inclusive education.

Abstract
The increasing complexity of wireless technologies, such as Wi-Fi, presents significant challenges for Rate Adaptation (RA) due to the large configuration space of transmission parameters. While extensive research has been conducted on RA for low-mobility networks, existing solutions fail to adapt in Flying Networks (FNs), where high mobility and dynamic wireless conditions introduce additional uncertainty. We propose Linear Upper Confidence Bound for RA (LinRA), a novel Contextual Bandit-based approach that leverages real-time link context to optimize transmission rates in predictable FNs, where future trajectories are known. Simulation results demonstrate that LinRA converges $\mathbf {5.2\times }$ faster than benchmarks and improves throughput by 80% in Non Line-of-Sight conditions, matching the performance of ideal algorithms.

Abstract
The challenge of engineering education is to transform engineering students into agents of innovation and well-being. In addition to solid scientific and technical knowledge, critical thinking, problem-solving and interpersonal competencies, it implies the ability to design and implement solutions supported by ethical and sustainability principles. With this goal in mind, the European Project Semester (EPS) provides a student-centred project-based learning framework. It is offered by a group of European higher education institutions, including the Instituto Superior de Engenharia do Porto (ISEP), the engineering school of the Polytechnic of Porto. Students work in teams of four to six, from different fields of study and nationalities, to design solutions to problems that affect individuals, society or the planet, taking into account the state of the art, the market and the ethical and sustainability implications of their decisions. These solutions are then implemented in a proof-of-concept prototype. Most of the projects address problems in education, the environment, food production and smart cities and have a strong educational, ethical and sustainability drive, encouraging students to develop sustainability competencies. This work analyses team papers of illustrative EPS@ISEP projects searching for evidences of the development of sustainability competencies. The proposed method maps keywords related to the sixteen United Nations Sustainable Development Goals to the contents of team papers by applying natural language processing and reusing the list of SDG keywords proposed by Auckland University. The results confirm EPS@ISEP fosters sustainability competencies in engineering undergraduates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Epidermal Growth Factor Receptor (EGFR) is one of the most frequently mutated genes in lung cancer. Its mutation status characterization is crucial for personalized treatment in Non-Small-Cell Lung Cancer (NSCLC). Biopsy is the gold standard for characterizing the EGFR mutation status. However, it is an invasive time-consuming method and is often burdensome or even impractical for some patients. Therefore, it is of utmost importance to identify alternative non-invasive methods for classifying this mutation. Computed Tomography (CT) images represent a non-invasive, safer and faster method to directly characterize lung cancer. This study developed a comprehensive radiomic approach for EGFR mutation classification using CT images, in which two preprocessing strategies were compared and five machine learning algorithms were evaluated across different datasets. We analyzed two independent datasets individually and combined, implementing lung containing nodule versus bounding box around nodule preprocessing approaches. Radiomic features were extracted using PyRadiomics and selected through Principal Component Analysis (PCA) (65-95% variance thresholds) and pairwise correlation filtering. The results demonstrated that the lung with nodule strategy achieved better and more consistent performance compared to the bounding box around the nodule method. The best performance (AUC=0.780) was achieved using Random Forest with correlation filtering. The results suggest that radiomics may be a potential support tool for EGFR classification when biopsy is not feasible or recommended. This would enable safer and more efficient personalized treatment. Nevertheless, the results underscore the need for larger, diverse datasets to improve model robustness for characterizing such complex and variable information before clinical integration. © 2025 IEEE.