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.

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 320x320 achieves the best balance between processing speed and detection quality in stereo-scopic 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.

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.

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.

Abstract
The design and development of consumer products require an interdisciplinary approach, often constrained by time-consuming prototyping and manual decision-making processes. As product complexity increases and market demands evolve, the need for automation and intelligent collaboration becomes evident. This paper presents a case study on the design and virtual validation of a premium pen using a multi-agent system, leveraging the integration of large language models (LLMs) and software agents. This combination enables a rational representation of human expertise and interactions, streamlining the design process while enhancing adaptability. Using CrewAI, agents were configured with specialized tasks, collaborating to optimize design, select sustainable materials, and establish quality standards. The agents generated a markdown report and a 3D simulation using Blender and Python, ensuring efficient coordination for an ergonomic, sustainable, high-quality pen. By modeling the rational behavior of human experts, the system demonstrated how LLMs and multi-agent coordination can reduce decision overhead and improve collaboration. The results show that multi-agent systems streamline product development by reducing decision overhead, improving task delegation, and enhancing collaboration. The final design met strict virtual quality standards and aligned with market preferences. This study demonstrates the role of multi-agent systems and LLM integration in Industry 4.0, supporting digital prototyping and virtual simulations to replace traditional physical prototyping.

Abstract
The evolving landscape of software development demands that software testers continuously adapt to new tools, practices, and acquire new skills. This study investigates software testing competency needs in industry, identifies knowledge gaps in current testing education, and highlights competencies and gaps not addressed in academic literature. This is done by conducting two focus group sessions and interviews with professionals across diverse domains, including railway industry, healthcare, and software consulting and performing a curated small-scale scoping review. The study instrument, co-designed by members of the ENACTEST project consortium, was developed collaboratively and refined through multiple iterations to ensure comprehensive coverage of industry needs and educational gaps. In particular, by performing a thematic qualitative analysis, we report our findings and observations regarding: professional training methods, challenges in offering training in industry, different ways of evaluating the quality of training, identified knowledge gaps with respect to academic education and industry needs, future needs and trends in testing education, and knowledge transfer methods within companies. Finally, the scoping review results confirm knowledge gaps in areas such as AI testing, security testing and soft skills.