Abstract
Abandoned mines are among the main sources of long-term soil contamination, often leaving behind persistent concentrations of potentially toxic elements (PTE) that pose environmental and health risks. Ribeiro da Serra Sb-Au mine, in Portugal, active from 1858 to 1890, has left a significant environmental legacy. This study mapped the spatial distribution of mine processing residues, elemental characterisation distinguishing between anthropogenic and natural enrichment by determining soil sample concentrations of PTE, Hg mobility, and Total Organic Carbon (TOC) quantification. Multivariate analysis, spatial interpolation and comparison with Enrichment Factor were employed with the aim of understanding the distributions and sources of PTEs in the soils. Mercury is still present at the site, revealing high mobile (18.72 mg kg(-1)) and semi-mobile (3.58 mg kg(-1)) concentrations accumulated in waste piles, a remnant of Au amalgamation processes. High Hg concentrations (20.75 mg kg(-1)) pose significant environmental risks, even after more than a century, such as bioaccumulation potential and soil toxicity. Also, there are high concentrations of Pb (449.03 mg kg(-1)) in the waste piles from the Sb processing. This study highlights the critical importance of interpreting natural enrichment values of elements of Hg (EF = 267) and Pb (EF = 18) to discern pollution pathways resulting from mining and processing activities. Interpretation of natural enrichment values leads to a more accurate evaluation of environmental contamination and its sources. The Enrichment Factor shows that Sb (EF = 133) and Hg (EF = 267) are the elements that present extremely high enrichment (EF > 40).

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Abstract
Deep learning has been extensively applied to medical imaging tasks over the past years, achieving outstanding results. However, the obscure reasoning of the models and the lack of supportive evidence causes both clinicians and patients to distrust the models' predictions, hindering their adoption in clinical practice. In recent years, the research community has focused on developing explanations capable of revealing a model's reasoning. Among various types of explanations, example-based explanations emerged as particularly intuitive for medical practitioners. Despite the intuitiveness and wide development of example-based explanations, no work provides a comprehensive review of existing example-based explainability works in the medical image domain. In this work, we review works that provide example-based explanations for medical imaging tasks, reflecting on their strengths and limitations. We identify the absence of objective evaluation metrics, the lack of clinical validation and privacy concerns as the main issues that hinder the deployment of example-based explanations in clinical practice. Finally, we reflect on future directions contributing towards the deployment of example-based explainability in clinical practice.

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Abstract
This study aims to map and compare the demand for green skills across four selected African countries by analyzing online job vacancies. Accordingly, the study addresses four research questions: (RQ1) how green skills demand has evolved over time; (RQ2) which sectors exhibit the highest demand for green skills; (RQ3) which occupations are most associated with green skills; and (RQ4) which green competencies are most frequently requested by employers. A Big Data and Labour Market Intelligence approach is employed based on secondary data provided by the online job vacancies (OJV). The results reveal a general upward trend in green skills demand, although with significant cross-country variation. Sectorally, sustainable energy dominates across all countries, followed by more context-specific areas such as agriculture, tourism, and production. At the occupational level, environmental engineers and other technical professions are most strongly associated with green skills. The thematic analysis highlights renewable energy, energy efficiency, and environmental sustainability as the most prominent skill domains, alongside emerging competencies in sustainable mobility, circular economy, and green digital skills. The study contributes to the literature by providing empirical evidence from an underexplored African context and demonstrating the value of online job vacancy data for monitoring labour market transformations.

Abstract