Abstract

Abstract
This article presents a Ph.D. research proposal for the automation of Digital Twin construction in industrial contexts through the semantic integration of heterogeneous data. The approach combines Large Language Model with the Asset Administration Shell framework to extract and map technical information from structured and unstructured sources (such as sensors, manuals and ERP/MES systems) into standardized submodels. The methodology includes four stages: data collection, semantic mapping using, organization into submodels and integration into Digital Twins. Initial tests with simulated data show the ability of LLMs to identify equivalent technical terms and generate structured data compatible with Asset Administration Shell. Ongoing work includes future activities with data from industrial partners, development of evaluation metrics and analysis with domain experts. The aim is to reduce manual modeling work, support interoperability and enable the construction of scalable Digital Twin in line with Industry 4.0 frameworks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
The ongoing energy transition and the rapid electrification of transport increase the importance of integrating renewable energy sources into smart mobility systems. Among these, solar energy plays a central role, but the variability of solar radiation poses significant challenges for planning electric vehicle (EV) charging and ensuring the reliable operation of transport networks. This work addresses these challenges by combining Big Data approaches and High-Performance Computing (HPC) to improve solar radiation forecasting and assess its implications for sustainable transport as a novelty from previous works. A Long Short-Term Memory (LSTM) neural network was the focus, and it was trained to predict key meteorological variables - global solar radiation, temperature, and wind speed - using both the original dataset of 13 years and expanded datasets of up to 130 years, generated to simulate Big Data scenarios. Forecasting performance remained stable across datasets, with R2 values above 0.85 for all variables. The best predictive results were obtained for the original dataset, achieving R2 = 0.9884 for solar radiation, while the HPC reduced execution time compared to conventional desktop environments. These results demonstrate that larger datasets improve model scalability and robustness, but significantly increase computational demands. The Deucalion supercomputer achieved the best performance, processing the largest dataset (130 years) in 44.24 minutes, while the same task on a Ryzen 7 required 51.00 minutes. The proposed approach highlights the potential of integrating Big Data and HPC to support EV charging optimisation, smart grid operation, and sustainable mobility strategies, contributing to faster, more reliable, and data-driven decision-making in the energy-transport ecosystem. © 2025 IEEE.

Abstract
This article presents a real-time object detection and distance estimation system implemented on a low-cost platform. The system uses a Raspberry Pi 5 and two cameras in a stereoscopic configuration to capture pairs of images. Object detection is performed using YOLO neural networks and distance estimation is based on the disparity between the centers of the detected bounding boxes. The system is evaluated in terms of detection performance, inference speed and depth estimation accuracy. Three YOLO models (YOLOv8n, YOLO11n and YOLO12n) are tested at different resolutions. Among them, the YOLO11n with a resolution of 320×320 achieves the best balance between processing speed and detection quality in stereoscopic operation. The system has a low error in depth estimation at close range, with absolute errors of less than 1.2 cm up to 60 cm. At greater distances, accuracy is affected by the reduction in the size of the bounding box, which limits the reliability of the disparity. Possible improvements include using segmentation-based localization and optimizing the stereo configuration. The proposed system is suitable for short-range applications in controlled environments and serves as a basis for future improvements in embedded vision systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Product design is a complex and iterative process that requires the balance of multiple constraints, such as material selection, manufacturability, regulatory compliance, and structural integrity, among others. Traditional design workflows follow a human-driven approach, limiting efficiency, adaptability, and the ability to quickly respond to evolving limitations. This paper introduces an agentic approach to product design, leveraging multi-agent systems to distribute and automate design tasks dynamically. To demonstrate this methodology, a hypothetical enclosure design is used as a guiding example, demonstrating how agents interact to generate product specifications, select materials, validate structural properties, assess manufacturability, and perform other relevant tasks throughout the design process. To implement this framework, CrewAI is utilized as an agent coordination system that enables the structured definition of roles and execution of tasks for autonomous agents. In the final section, a case study is presented, focusing on the design of a parallelepiped enclosure, applying the proposed framework in a simulated environment. Our findings highlight the advantages of agent-based collaboration in product design, showcasing its potential to optimize workflows, reduce development time, and improve adaptability to changing requirements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Efficient battery management in electric vehicles plays a key role in the transition to more sustainable and energy efficient mobility. This article presents a proposal for a modular framework to optimise charging and energy consumption based on solar radiation prediction. The solution integrates three main components: climate prediction models, battery behaviour simulation, and optimisation algorithms for decision making. This approach aims to dynamically adapt charging strategies to maximise vehicle autonomy and reduce energy waste. The modularity of the framework allows it to be applied to different vehicle types and operating contexts, ensuring flexibility and scalability. In addition, preliminary studies on solar radiation forecasting have already been carried out, providing a basis for future development of the system. The implementation of this approach represents an important step towards more efficient energy management in electric vehicles, contributing to the reduction of environmental impact and the promotion of sustainable electric mobility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.