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.

Abstract

Abstract
The growing use of renewable energy sources has made the development of efficient and sustainable microgrids increasingly important. In this context, direct current (DC) microgrids have emerged as a suitable solution, as they are more efficient and compatible with renewable energy generation and storage systems. A mixed-integer linear programming (MILP) model is proposed to optimize the design of DC microgrids comprising photovoltaic modules, wind turbines and energy storage systems. The model determines the optimal number of energy storage systems and generation components. Furthermore, energy exchanges with the main grid are also determined over a long-term planning horizon. The proposed model is evaluated using five case studies corresponding to different storage configurations and grid interaction scenarios. These case studies are based on seasonal load profiles derived from actual consumption data and solar irradiance data collected between 2019 and 2023. Technical and financial metrics, such as net present value (NPV), levelized cost of electricity (LCOE), internal rate of return (IRR), and payback period (PBP), are used to evaluate the system’s performance. Results show that photovoltaic-based configurations provide the most cost-effective solutions under current market conditions, while the inclusion of storage systems improves long-term profitability despite higher initial investment. Sensitivity analysis highlights the strong influence of energy prices and equipment costs on the optimal configuration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.

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.