Abstract
The transition to circular business models poses significant challenges, particularly for Small and Medium Enterprises (SMEs). These challenges arise from different perspectives. Strategic alignment and technological barriers are just two of them. This paper aims to explore how Industry 4.0 technologies and Enterprise Architectures can facilitate the implementation of circular business models. By analyzing their role in overcoming key obstacles, the study explores the potential of these technologies in driving sustainable business transformation. The findings indicate that while integrating circularity into business practices remains complex, Enterprise Architectures, through the adoption of Industry 4.0 technologies, can mitigate some barriers. Ultimately, the synergy between technological innovation and circular business models can accelerate the shift towards sustainability.

Abstract
Objective: To empirically investigate the relationship between education quality and technological progress in the business sector at different stages of economic development. Research Design & Methods: We divided 160 countries into four groups by GDP per capita. The research period was 2007–2021. We use Spearman’s correlation analysis to verify associations between nine indicators for education quality and ten indicators for technological progress. Findings: Our outcomes show that if education quality does not improve, countries do not move up the economic development ladder. Adult literacy, primary education quality, adult skills, and women’s average years in school have the strongest influence on technological progress. Implications?/?Recommendations: Our paper contains many implications for those seeking to improve social well-being. For example, governments should ensure that women have access to education on equal terms with men. Otherwise, they lose an important source of technological progress and impede the development of human capital. Greater emphasis should be placed on learning how to write and describe reality, read with comprehension, perform simple calculations without a calculator, and teach various learning methods. In the absence of these, the skills of primary, secondary and higher education graduates will not improve. Contribution: The outcomes of our research, both theoretical and empirical, create a multi-faceted approach to the issue of the mutual influence of education and technological progress. They allow us to look at this problem from the perspective of subsequent stages of economic development.

Abstract
With the proliferation of ODR service providers, there is a critical necessity to establish mechanisms supporting their functioning, particularly while designing ODR processes. This article aims to examine the impact of process modelling using BPMN, and of its relevance in the integration of AI into ODR processes within the EU. BPMN allows a meticulous depiction of all the ODR process steps, stakeholders, and underlying data in structured formats that are readable and interpretable by both humans and AI, which enables its integration. The advantages include predictive analysis, identification of opportunities for continuous improvement, operational efficiency, cost and time reduction, and enhanced accessibility for self-represented litigants. Additionally, the transparency afforded by explicitly incorporating AI in BPMN notation fosters a clearer comprehension of processes, facilitating management and informed decision-making. Nevertheless, it remains imperative to address ethical concerns such as algorithmic bias, fairness, and privacy.

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
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.

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.