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
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
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.

Abstract
The interaction between humans and technology has always been a key determinant factor of adoption and efficiency. This is true whether the interaction is with hardware, software or data. In the particular case of Information Retrieval (IR), recent developments in Deep Learning and Natural Language Processing (NLP) techniques opened the door to more natural and efficient IR means, no longer based on keywords or similarity metrics but on a distributed representation of meaning. In this paper we propose an agent-based architecture to serve as an interface with industrial systems, in which agents are powered by specific Large Language Models (LLMs). Its main goal is to make the interaction with such systems (e.g. data sources, production systems, machines) natural, allowing users to execute complex tasks with simple prompts. To this end, key aspects considered in the architecture are human-centricity and context-awareness. This paper provides a high-level description of this architecture, and then focuses on the development and evaluation of one of its key agents, responsible for information retrieval. For this purpose, we detail three application scenarios, and evaluate the ability of this agent to select the appropriate data sources to answer a specific prompt. Depending on the scenario and on the underlying model, results show an accuracy of up to 80%, showing that the proposed agent can be used to autonomously select from among several available data sources to answer a specific information need. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.