Abstract
Nim is a mathematical combinatorial game in which two players take turns removing, or nimming, objects from distinct heaps or piles Although its rules are simple, which makes it extremely easy to play, it requires a solid strategic reasoning in order to win against experienced players. This study presents an optimised strategic approach to the game of Nim, which represents the guaranteed winning strategy for this game for the first player to take action. The proposed approach is a fundamental combinatorial game rooted in Boolean algebra and the XOR operation. Unlike traditional strategies that solely rely on XOR calculations to determine winning and losing positions, this research identifies and analyses anomalous strategic behaviours that challenge conventional Nim theory, revealing previously unexplored patterns in specific game configurations. To validate these findings, a Python-based application has been developed, implementing the proposed strategy to ensure consistent victory. The algorithm systematically applies XOR calculations, executes optimal moves, and dynamically adapts to anomalies, demonstrating how these irregularities can be leveraged for strategic advantage. This computational validation reinforces the theoretical framework and provides new insights into the limitations and extensions of classical Nim strategies. Beyond its implications for Nim, this research highlights the broader potential of AI-driven decision-making in combinatorial games. By demonstrating how algorithmic intelligence can analyse game states, predict outcomes, and refine strategies, this study contributes to advancements in artificial intelligence, optimisation algorithms, and complex strategic decision-making models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract

Abstract
Explainable Artificial Intelligence (XAI) seeks to enhance the interpretability of Artificial Intelligence (AI) systems, ensuring that algorithmic decisions and their underlying data are comprehensible to non-technical stakeholders. While advanced Machine Learning (ML) models, such as deep neural networks, have significantly improved AI capabilities, their complexity poses challenges for XAI, particularly in handling large datasets required for training and interpretation. In particular, the application of Shapley Additive Explanations (SHAP), although widely recognized for its effectiveness, often incurs a high computational cost when applied to large-scale data. Addressing this issue, our previous work proposed a novel approach that leverages K-Means clustering to identify representative data instances, applied after the forecasting phase to refine SHAP-based explanations and reduce computational costs while preserving their fidelity. This extended study further optimizes the clustering strategy and evaluates its applicability across broader use cases in sustainable energy systems. We apply our method to forecast photovoltaic (PV) generation in buildings, a critical aspect of energy management in e-mobility and smart grids. The results show that clustering reduces execution time by more than 50 % compared to random sampling while maintaining comparable explanatory stability. These findings highlight the potential of data-driven clustering techniques in enhancing the explainability of ML models in energy forecasting, contributing to more accessible and practical AI solutions for real-world applications.

Abstract
This paper explores the potential of the educational gamification platform known as SCORE as a novel solution to address challenges related to student disengagement and the increasing preference for gaming. Faced with observed disinterest among first-year Computer Engineering students, particularly intensified during the Covid-19 era, the study advocates for integrating the educational gamification platform to create a dynamic and engaging learning environment. SCORE is presented as an innovative alternative to conventional teaching methods, fostering deeper understanding and motivation among students. Positioned to catalyze holistic student development, encompassing critical thinking and problem-solving skills, SCORE emerges as a leading player in the evolving landscape of educational gamification. The document provides a comprehensive overview of the motivating factors for this investigation, laying the groundwork for a detailed analysis of SCORE and the role of educational games in effective teaching methods. Anticipated outcomes encompass enriched pedagogical practices and a solid foundation for future research endeavors. Positioned as one among many alternatives, SCORE contributes to the ongoing discourse on innovative teaching methods, offering valuable insights for educators and researchers exploring ways to enhance the learning experience. With the evolution of technology, SCORE, alongside other educational games, aims to take a significant step forward in academic terms, enabling students to achieve the best possible results while remaining motivated in their academic journey. © 2025 Elsevier B.V., All rights reserved.

Abstract
This study delves into the critical role of third-party applications in enhancing motorcycle interface design, particularly focusing on their potential to revolutionize the rider experience through innovative UI/UX designs. As motorcycles increasingly serve as primary modes of transportation in urban environments, safety concerns escalate, needing effective solutions. The research scrutinizes the main safety challenges faced by motorcycle riders, the influence of thirdparty applications on safety outcomes, and the advantages these applications offer over traditional dashboards. In this context, it is presented a Systematic Literature Review (SLR) methodology. Specifically, articles were selected based on predefined inclusion and exclusion criteria. Results indicate that third-party applications provide customizable solutions that not only offer real-time information about the motorcycle's condition but also mitigate safety risks by minimizing distractions. However, the literature reveals a lack of studies specifically addressing these applications, underscoring their potential as pioneering initiatives to reinforce motorcycle safety.