Abstract
The current era where living demands an accelerated digital transition mainly focused on encouraging a smarter, healthier, and more sustainable mobility, in all its dimensions – a must concern for the young generations. The convergence through several digital services and APP can be an attitudes and perception changer within the group of academic mobility users’, promoting a more sustainable and better mobility choices that impact on the academic user’s mobility routines. Thus, encouraging a global shift to shared and active mobility services and systems bringing significant contributions to environmental sustainability and, also, to users’ health. The Academic Mobility as a Service (AMaaS) provide a digital service with mobility alternatives to support the academic population geographically located in different faculty campuses and Higher Education Institutions (HEI). The AMaaS applied to a restrict group is helpful to test innovative transport solutions and its high cybersecurity vulnerabilities. Despite the shortage of AMaaS case studies and the lack of security reference, it is imperative that a cybersecurity by design is planned and included in AMaaS design. In this paper AMaaS critical cybersecurity challenges, and potential risks are discussed and AMaaS Security by Design framework is described. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
PurposeThis paper aims to identify the critical success factors (CSFs) for managing remote project teams (RPT) within project environments. In other words, it focuses on identifying the crucial elements for the success of projects executed by RPT.Design/methodology/approachAn exploratory mixed-method was used combining a case study approach with the application of surveys. Document analysis and direct observation were also applied. The analyzed company is a well-known project-based company acting in the coffee industry and is justified due to its multilocation and multicultural perspectives.FindingsThrough an initial literature review, 93 CSFs were identified and then organized into 7 categories. The subsequent phase involved the relevance evaluation of the identified CSFs through surveys conducted in an international company. The first results analysis identified 20 CSFs. A deeper analysis identified the most relevant factors for each category (Project Managers, 33 factors; Team Leaders, 15; and Team Members, 29). Combining these results, 11 CSFs were identified.Originality/valueWith the trend of remote work that is being kept after the pandemic, this study contributes to identify the most relevant issues that must be taken into account in managing remote teams. By identifying those issues, or CSFs, managers and team members might focus on the most relevant factors.

Abstract
In the current era of digital transformation, adopting circular business models that blend circularity principles with advanced digital technologies, is fundamental for sustainable industrial practices. This paper suggests a semantic model for a Digital Twin based on an Asset Administration Shell. It also explores the Digital Product Passport topic since this will be the final goal for the Digital Twin. The Digital Product Passport serves as a complete digital record of the product life cycle to improve traceability and circularity. The Asset Administration Shell provides a standardized digital representation of assets, facilitating interoperability and fluid data exchange. By taking advantage of a Digital Twin, industries can optimize performance and predict product needs. Moreover, it enriches the Digital Product Passport with updated and accurate data, facilitating traceability and efficient product management. The application of semantic models ensures a consistent interpretation of data across all platforms, increasing the reliability of digital interactions and interoperability. This article explains the potential of these technologies to promote a circular economy, focusing in the particular case of the Digital Product Passport. © 2025 The Author(s).

Abstract
Explainable AI (xAI) emerged as one of the ways of addressing the interpretability issues of the so-called black-box models. Most of the xAI artifacts proposed so far were designed, as expected, for human users. In this work, we posit that such artifacts can also be used by computer systems. Specifically, we propose a set of metrics derived from LIME explanations, that can eventually be used to ascertain the quality of each output of an underlying image classification model. We validate these metrics against quantitative human feedback, and identify 4 potentially interesting metrics for this purpose. This research is particularly useful in concept drift scenarios, in which models are deployed into production and there is no new labelled data to continuously evaluate them, becoming impossible to know the current performance of the model.

Abstract
Arterial stiffness (AS) is a well-established predictor of cardiovascular events, including myocardial infarction and stroke. One of the most recognized methods for assessing AS is through arterial pulse wave velocity (aPWV), which provides valuable clinical insights into vascular health. However, its measurement typically requires specialized equipment, making it inaccessible in primary healthcare centers and low-resource settings. In this study, we developed and validated different machine learning models to estimate aPWV using common clinical markers routinely collected in standard medical examinations. Thus, we trained five regression models: Linear Regression, Polynomial Regression (PR), Gradient Boosting Regression, Support Vector Regression, and Neural Networks (NNs) on the EVasCu dataset, a cohort of apparently healthy individuals. A 10-fold cross-validation demonstrated that PR and NN achieved the highest predictive performance, effectively capturing nonlinear relationships in the data. External validation on two independent datasets, VascuNET (a healthy population) and ExIC-FEp (a cohort of cardiopathic patients), confirmed the robustness of PR and NN (R- (2)> 0.90) across different vascular conditions. These results indicate that by using easily accessible clinical variables and AI-driven insights, it is possible to develop a cost-effective tool for aPWV estimation, enabling early cardiovascular risk stratification in underserved and rural areas where specialized AS measurement devices are unavailable.

Abstract
The rapid integration of Machine Learning (ML) in organizational practices has driven demand for substantial computational resources, incurring both high economic costs and environmental impact, particularly from energy consumption. This challenge is amplified in dynamic data environments, where ML models must be frequently retrained to adapt to evolving data patterns. To address this, more sustainable Machine Learning Operations (MLOps) pipelines are needed for reducing environmental impacts while maintaining model accuracy. In this paper, we propose a model reuse approach based on data similarity metrics, which allows organizations to leverage previously trained models where applicable. We introduce a tailored set of meta-features to characterize data windows, enabling efficient similarity assessment between historical and new data. The effectiveness of the proposed method is validated across multiple ML tasks using the cosine and Bray-Curtis distance functions, which evaluate both model reuse rates and the performance of reused models relative to newly trained alternatives. The results indicate that the proposed approach can reduce the frequency of model retraining by up to 70% to 90% while maintaining or even improving predictive performance, contributing to more resource-efficient and sustainable MLOps practices.