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.

Abstract
This article presents an in-depth literature review on the role of Metaheuristics in Operations Scheduling Problems. This paper intends to evaluate the use of Metaheuristics, focusing on its developments in an industrial setting. In the first part a literature review was executed on the significance of Operations Scheduling and its different types, as well as Metaheuristics, providing historical context to both of these topics. A bibliometric analysis is also performed, through a refined query, to understand the growth in number of articles published by year on this work's topic as well as their main areas of investigation and the most important keywords present. Then, two case studies are presented in detail, conveying the effectiveness of Metaheuristics in solving complex Operations Scheduling Problems. More case studies, embracing a diverse plethora of Metaheuristics and different types of Scheduling Problems, are also briefly referred in order to reinforce this point. At the end, a discussion about the future of Metaheuristics, their applications and advantages in industry settings is held, presenting the final thoughts, predicting a significant growth in their usage, establishing these algorithms as a pillar of industrial production in the future.

Abstract
State-based Conflict-Free Replicated Data Types (CRDTs) are widely used in distributed systems to ensure high availability without coordination. However, their naive synchronization strategy, transmitting the full state, incurs high communication costs. In this paper, we: (1) propose ConflictSync, a digest-driven synchronization algorithm, which reduces total data transfer by up to 18x compared to full-state transmissions; (2) formulate state-based CRDT synchronization as set reconciliation over irredundant join decompositions; (3) generalize Rateless Set Reconciliation for variable-sized elements, at the cost of an additional communication step; (4) introduce a new generic set reconciliation solution, integrating Bloom Filters with rateless IBLTs; (5) experimentally evaluate the novel synchronization strategies.

Abstract
Spectral analysis of cultural heritage materials offers valuable insights into the restoration and preservation of historical artifacts, revealing details about the materials used and the manufacturing techniques employed. However, given their historical and artistic significance, the extraction of elemental information from these fragile samples poses a unique challenge, as these objects must be examined using minimally invasive methods to prevent irreversible damage. Laser-induced Breakdown Spectroscopy (LIBS) is one such technique, providing a rapid and detailed elemental characterization. Yet, extensive LIBS analysis can still compromise the integrity of these delicate objects. In this work, a novel approach that integrates spectral and RGB data clustering to significantly reduce the number of LIBS measurements required is introduced. By segmenting the material into visually and chemically distinct clusters, this method enables targeted LIBS analysis using only a few representative shots per cluster, thus preserving the integrity of cultural heritage artifacts while still delivering reliable compositional insights. (c) 2026 The Author(s). Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)