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.

Abstract

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.

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Abstract
In the past years, the prevalence of neurodegenerative diseases has increased, highlighting the urgent need to better understand and combat these diseases. Innovative ultrasound treatments have shown promising results but require further investigation, particularly regarding the penetration of acoustic waves into the brain. This work focus on developing a hydrophone to study acoustic wave penetration in biological tissue, combining computational simulations and experimental testing. The hydrophone design was optimized for accurate measurements of wave interactions with tissue, and experiments using gelatine and biological tissue samples validated its performance. The results show the hydrophone’s potential to measure acoustic wave interactions in heterogeneous tissues, providing a foundation for optimizing therapeutic ultrasound in neurodegenerative diseases. Further refinements are needed to improve accuracy in more complex conditions. © 2026, Springer Science and Business Media Deutschland GmbH. All rights reserved.

Abstract
The rapid adoption of Large Language Models (LLMs) is transforming research, education, software development and everyday life. As their use grows, so does the diversity of available models, from general-purpose to code-oriented LLMs that can run both in data centers and on edge devices. Several benchmarks have emerged to evaluate their performance in code generation and completion tasks, yet their energy and time efficiency remain underexplored. This paper evaluates five local LLMs on HumanEval-X and MBPP+ to analyze their accuracy, runtime and energy consumption under CPU-only inference, reflecting realistic on-device deployment scenarios where GPUs are unavailable. The results reveal clear trade-offs between effectiveness and efficiency: while some models achieve higher accuracy, others deliver comparable results with substantially lower energy use. In particular, 3-shot prompting consistently improves runtime and energy efficiency compared to 0-shot, without sacrificing code quality. These findings emphasize that prompt design and model selection must be considered together when deploying LLMs for coding tasks and call for the creation of practical prompt-efficiency guidelines to support more sustainable and efficient use of local LLMs.