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
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
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.

Abstract
Switched Reluctance Motors (SRMs) are becoming increasingly popular for various applications, including automotive applications. However, challenges such as torque ripple and vibration persist, limiting their performance. This chapter investigates the application of intelligent control strategies, particularly fuzzy logic, to mitigate these issues. Fuzzy logic modeling does not require an accurate mathematical model which is very difficult to obtain from a SRM because of its inherit nonlinearities. In this work a Fuzzy Logic Controller (FLC) applied to the speed control of an SRM, highlighting the advantages of FL over traditional methods in terms of flexibility and performance. A comparison is made between the FLC, a Sliding Mode Control (SMC), and a Proportional Integral (PI) controller. Simulation results using MATLAB/Simulink show that the FLC substantially reduces torque ripple, offering better overall performance in terms of smoothness and robustness under varying operational conditions. The findings demonstrate that FLC offers a more effective solution than conventional approaches for SRM applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.