Abstract
The advent of large Machine Learning models and the steep increase in the demand for AI solutions occurs at the same point in time in which policies are being enacted to implement more sustainable processes in virtually every sector. This means there is a need for more, better and larger models, which require significant computational resources, while at the same time a call for a decrease in the energy spent in the processes associated to MLOps. In this paper we propose a reduced set of meta-features that can be used to characterize sets of data and their relationship with model performance. We start from a large set of 66 features, and reduce it to only 10 while maintaining the strength of this relationship. This ensures a process of meta-feature extraction and prediction of model performance that is in line with the desiderata of Frugal AI, allowing to develop more efficient ML processes.

Abstract
Digitalisation is mandatory for today’s companies. Living in the Era of Industry 4.0, the phenomenon of digital transformation cannot be ignored. Intending to support manufacturing companies in their digitalisation processes, the present paper reflects the work that has been carried on, to support the digital transition for manufacturing companies in the region of Tâmega e Sousa. This region is considered to be an industrial region located in the North of Portugal, but lagging in terms of digital technology adoption. In a theoretical framework, it is expected to identify the most relevant factors to promote a successful digital strategy. Supported by a Science Design methodology, a platform was developed to support the measurement of the maturity or digital companies’ readiness levels. To collect the necessary data were performed questionnaires. First, in a face-to-face approach and later through the platform developed. The (preliminary) results are based on a sample of 53 companies (pilot test). From this data, it was possible to identify some trends: 1) some behaviours indicate that the region is still in the digitisation phase; 2) the digitisation focus is in the processes dimension; 3) even performing a digital transition, companies do not invest in in-house IT solutions. Copyright © 2025 Inderscience Enterprises Ltd.

Abstract
The field of AI Ethics has recently gained considerable attention, yet much of the existing academic research lacks practical and objective contributions for the development of ethical AI systems. This systematic literature review aims to identify and map objective metrics documented in literature between January 2018 and June 2023, specifically focusing on the ethical principles outlined in the Ethics Guidelines for Trustworthy AI. The review was based on 66 articles retrieved from the Scopus and World of Science databases. The articles were categorized based on their alignment with seven ethical principles: Human Agency and Oversight, Technical Robustness and Safety, Privacy and Data Governance, Transparency, Diversity, Non-Discrimination and Fairness, Societal and Environmental Well-being, and Accountability. Of the identified articles, only a minority presented objective metrics to assess AI ethics, with the majority being purely theoretical works. Moreover, existing metrics are primarily concentrating on Diversity, Non-Discrimination and Fairness, with a clear under-representation of the remaining principles. This lack of practical contributions makes it difficult for Data Scientists to devise systems that can be deemed Ethical, or to monitor the alignment of existing systems with current guidelines and legislation. With this work, we lay out the current panorama concerning objective metrics to quantify AI Ethics in Data Science and highlight the areas in which future developments are needed to align Data Science projects with the human values widely posited in the literature.

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.

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.