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
This paper provides an overview of the application potential of artificial intelligence (AI) in power systems and points towards prospective developments in the fields of AI that are promised to play a transformative role in the evolution of power systems. Among the basic requirements, also imposed by regulation in some places, are trustworthiness and interpretability. Large language models, foundation models, as well as neuro-symbolic and compound AI models, appear to be the most promising emerging AI paradigms. Finally, the trajectories along which the future of AI in power systems might evolve are discussed, and conclusions are drawn.

Abstract
In this study, we address the inventory decision problem of ameliorating goods by explicitly incorporating a demand spillover effect between product categories - an interaction that has received little attention in operations management. We first empirically demonstrate the existence of this spillover using multi-year sales data from 11 Port wine brands across 86 markets. Building on these insights, we integrate the spillover effect into a stochastic inventory decision model for a (Port) wine seller who must decide whether to sell existing inventory or continue aging it to offer higher-quality products in the future. The problem is formulated as a Markov Decision Process and solved using a forecast-based Deterministic Lookahead (DLA) approach and a Proximal Policy Optimization (PPO) algorithm. Our results show that accounting for the spillover effect can increase profits by up to 1.31%, and that both proposed solution methods outperform the myopic strategy currently applied by producers. While the DLA policy performs best under high forecast accuracy, the PPO algorithm proves more robust when uncertainty is high. The study contributes to bridging marketing and operations perspectives by quantifying the economic impact of spillover effects and providing decision-support tools for managing aged inventory under demand uncertainty.

Abstract
In modern industrial environments, ensuring the quality of manufactured components is critical, particularly when dealing with reflective surfaces that hinder conventional inspection techniques. Although deep learning-based methods offer robust solutions for visual defect detection, their performance often hinges on the availability of substantial annotated datasets. In industrial scenarios, labeling such datasets is costly and time-consuming. This study investigates applying sample selection techniques to reduce annotation efforts for porosity detection on machined aluminium parts. Several selection strategies were evaluated using a real-world dataset composed of high-resolution images, including uncertainty, diversity, random-based criteria, and hybrid combinations. The best-performing strategy, which combined entropy-based uncertainty, spatial diversity, and random-based, achieved an F1-score of 86.70% and a recall of 82.99% after ten iterations using only 2,400 annotated images, corresponding to 66.67% of the active learning pool. Although the fully supervised model achieved an F1-score of 88.84% and a recall of 86.30%, the proposed approach proved a competitive alternative. These results demonstrate that selective data annotation can significantly reduce labeling effort while maintaining reliable performance in defect detection, even under the challenging conditions posed by reflective industrial parts.