Abstract
A Renewable Energy Management System (REMS) is designed to enhance the operation and efficiency of renewable energy assets, such as wind and solar power, by addressing their inherent variability. Through integration with Supervisory Control and Data Acquisition (SCADA) systems, REMS facilitates real-time adjustments and forecast-based decisions, enabling grid security, optimizing energy dispatch, and maximizing economic benefits. This paper introduces a versatile active power control methodology for renewable energy plants, capable of operating across various time scales to address technical and market-driven requirements. The proposed framework processes inputs from power system measurements to generate forecasts using two distinct approaches, optimizing setpoints for energy dispatch and control processes. Four optimization methods—merit order, weighted allocation, proportional allocation, and linear optimization—are employed to maximize power utilization while adhering to system constraints. The approach is validated for two control intervals: 4 seconds, representing rapid response for converter-based resources, and 15 minutes, simulating broader operational adjustments for reserve provision programs. This dynamic and scalable control framework demonstrates its potential to enhance the management, efficiency, and sustainability of renewable energy systems. © The Institution of Engineering & Technology 2025.

Abstract
The rise of Industry 4.0 has revolutionized manufacturing by integrating real-time data analysis, artificial intelligence (AI), automation, and interconnected systems, enabling adaptive and resilient smart factories. Autonomous Mobile Robots (AMRs), with their advanced mobility and navigation capabilities, are a pillar of this transformation. However, their deployment in job shop environments adds complexity to the already challenging Job Shop Scheduling Problem (JSSP), expanding it to include task allocation, robot scheduling, and travel time optimization, creating a multi-faceted, non-deterministic polynomial-time hardness (NP-hard) problem. Traditional approaches such as heuristics, meta-heuristics, and mixed integer linear programming (MILP) are commonly used. Recent AI advancements, particularly large language models (LLM), have shown potential in addressing these scheduling challenges due to significant improvements in reasoning and decision-making from textual data. This paper examines the application of LLM to tackle scheduling complexities in smart job shops with mobile robots. Guided by tailored prompts inserted manually, LLM are employed to generate scheduling solutions, being these compared to an heuristic-based method. The results indicate that LLM currently have limitations in solving complex combinatorial problems, such as task scheduling with mobile robots. Due to issues with consistency and repeatability, they are not yet reliable enough for practical implementation in industrial environments. However, they offer a promising foundation for augmenting traditional approaches in the future.

Abstract
In this article it is performed an in-depth literature review of simulation in Industry 4.0. This paper intends to evaluate the use of simulation tools, focusing on its developments in Industry 4.0. In the first part a literature review was executed on the significance of simulation tools, such as Digital Twins, Discrete Event Simulation and Agent-Based Modelling, throughout several fields of study, giving special attention to manufacturing and production. A bibliometric analysis is also performed to understand the growth in number of articles published on simulation in Industry 4.0. Then, two case studies are presented, conveying the effectiveness of Digital Twins and Discrete Event Simulation in assisting process planning and control, and manufacturing automation. A few more case studies are also briefly referred in order to reinforce this point. At the end, a discussion about the future of simulation, its applications and advantages in industry settings is held, finally presenting the final thoughts, predicting an exponential growth in simulation uses, establishing this tool as a pillar of industrial production in the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This paper presents an optimization model for electric vehicle (EV) fleet charging under MIBEL (Iberian Electricity Market). The model integrates EV charging with day-ahead forecasting for grid energy prices, photovoltaic (PV) generation, and local power demand, combined with a battery energy storage system (BESS) to minimize total charging costs, reduce peak demand, and maximize renewable use. Simulations across Baseline, Certainty, and Uncertainty scenarios show that the proposed approach would reduce total charging costs by up to 49%, lower carbon emissions by 73.7%, and improve SOC compliance, while smoothing demand curves to mitigate excessive contracted power charges. The results demonstrate the economic and environmental benefits of predictive and adaptive EV charging strategies, highlighting opportunities for further enhancements through real-time adjustments and vehicle-to-grid (V2G) integration.

Abstract
Stroke is a leading cause of long-term disability worldwide, with survivors often facing significant challenges in regaining upper-limb functionality. In response, robotic rehabilitation systems have emerged as promising tools to enhance post-stroke recovery by delivering precise, adaptable, and patient-specific therapy. This paper presents a review of robotic interfaces developed specifically for upper-limb rehabilitation. It analyses existing exoskeleton- and end-effector-based systems, with respect to three core design pillars: assistance types, control philosophies, and actuation methods. The review highlights that most solutions favor electrically actuated exoskeletons, which use impedance- or electromyography-driven control, with active assistance being the predominant rehabilitation mode. Resistance-providing systems remain underutilized. Furthermore, no hybrid approaches featuring the combination of robotic manipulators with actuated interfaces were found. This paper also identifies a recent trend towards lightweight, modular, and portable solutions and discusses the challenges in bridging research prototypes with clinical adoption. By focusing exclusively on upper-limb applications, this work provides a targeted reference for researchers and engineers developing next-generation rehabilitation technologies.

Abstract
Covid-19 is a severe illness caused by the Sars-CoV-2 virus, initially identified in China in late 2019 and swiftly spreading globally. Since the virus primarily impacts the lungs, analyzing chest X-rays stands as a reliable and widely accessible means of diagnosing the infection. In computer vision, deep learning models such as CNNs have been the main adopted approach for detection of Covid-19 in chest X-ray images. However, we believe that handcrafted features can also provide relevant results, as shown previously in similar image classification challenges. In this study, we propose a method for identifying Covid-19 in chest X-ray images by extracting and classifying local and global percolation-based features. This technique was tested on three datasets: one comprising 2,002 segmented samples categorized into two groups (Covid-19 and Healthy); another with 1,125 non-segmented samples categorized into three groups (Covid-19, Healthy, and Pneumonia); and a third one composed of 4,809 non-segmented images representing three classes (Covid-19, Healthy, and Pneumonia). Then, 48 percolation features were extracted and give as input into six distinct classifiers. Subsequently, the AUC and accuracy metrics were assessed. We used the 10-fold cross-validation approach and evaluated lesion sub-types via binary and multiclass classification using the Hermite polynomial classifier, a novel approach in this domain. The Hermite polynomial classifier exhibited the most promising outcomes compared to five other machine learning algorithms, wherein the best obtained values for accuracy and AUC were 98.72% and 0.9917, respectively. We also evaluated the influence of noise in the features and in the classification accuracy. These results, based in the integration of percolation features with the Hermite polynomial, hold the potential for enhancing lesion detection and supporting clinicians in their diagnostic endeavors.