Abstract

Abstract
This study highlights the critical role of Large Language Model (LLM) in simplifying technical content and integrating visual data for accessible communication. It compares GPT-4 and Llama-3.2-90b-Vision-Preview, focusing on readability, semantic similarity, and multimodal interpretation using robust metrics like Flesch Reading Ease, Gunning Fog Index, and CLIP Score. GPT-4 retains key information and achieves high semantic and textual integration scores, making it more suitable for complex technical scenarios. Furthermore, LLaMA prioritizes readability and simplicity, outperforming in generating accessible captions. Both models show optimal performance with a temperature setting of 0.5, balancing simplicity and meaning preservation. The research underscores LLM potential to democratize technical knowledge across disciplines but notes precision and multimodal integration limitations. Future directions include fine-tuning for domain-specific applications and expanding input modalities to enhance accessibility and efficiency in real-world technical tasks.

Abstract
This study presents an innovative approach to assessing service quality in the water supply and wastewater treatment sectors, using directional Benefit-of-the-Doubt (BoD) models tailored to regulator needs. Unlike previous research, this work integrates the regulator preferences throughout the entire evaluation process, from selecting key performance metrics to determining reference weights and validating results through sensitivity analyses. A new index for the Assessment of the Quality of Services (AQS) was constructed using a set of indicators chosen by the regulator, ensuring a direct alignment with regulatory priorities. Additionally, the study examines the relationship between service quality and cost efficiency, the latter computed using the Data Envelopment Analysis (DEA) methodology, to address the inherent tension in the water sector between these often conflicting goals. By providing a comprehensive comparison of wholesale utilities' performance, the findings highlight that cost efficiency and service quality do not always align. This underscores the need for a balanced regulatory approach that fosters service quality improvements while maintaining cost control, promoting sustainable and effective management of the sector.

Abstract

Abstract
As industrial product development becomes increasingly complex and knowledge-intensive, the integration of Artificial Intelligence (AI) agents into design workflows offers great potential to improve efficiency and decision making. However, the opacity of current AI reasoning processes remains a major obstacle for adoption in engineering domains. This position paper explores the need for Explainable AI (XAI) within agentic design systems, proposing a conceptual architecture where agents, powered by Large Language Models (LLMs), not only perform domain-specific tasks, but also generate human-readable justifications for their decisions. Unlike black-box systems, these agents are designed to promote transparency, trust, and traceability, all of which are critical in high-stakes industrial contexts. Building upon the foundation of the Agentic Approach to Product Design, we outline how roles such as requirement analysis, material selection, and specification interpretation can be reimagined with explainability at their core. This work advocates for a shift towards interpretable, auditable AI assistants, capable of supporting collaborative engineering processes. An illustrative scenario is used to exemplify the practical value and challenges of agents supported by XAI. Future research directions are highlighted, including evaluation metrics for explainability and potential integrations into existing agent orchestration platforms such as CrewAI. As a conceptual position paper, this work aims to stimulate the development of explainable multi-agent design systems and guide future empirical validation in industrial contexts.

Abstract
Additive Layer Manufacturing, particularly Fused Deposition Modelling, faces significant batch loss risks during production. The traditional Concurrent Printing Mode produces all parts simultaneously (layer-by-layer, bottom-to-top), efficiently using printing space but risking complete batch failure if problems occur. In contrast, Sequential Printing Mode produces one part at a time, reducing the risk of total batch loss but utilising printing space less efficiently. In this work, we propose an algorithm that, given a set of parts, performs the nesting of the parts for Concurrent Printing Mode, and for the first time, for the Sequential Printing Mode. A no-fit polygon based approach is used to handle geometry between pairs of parts by using multiple horizontal 2D layer projections of 3D parts, to ensure non-overlapping constraints and prevent machine-part collisions. A Greedy Randomized Adaptive Search Procedure is proposed, tested and benchmarked against a commercial software, using a new set of real-world instances. The approach shows the ability to find high-quality solutions. The approach significantly reduces the number of batches, minimises waste, reduces manufacturing time, and promotes parts quality.