Abstract
PurposeThis study explores gamification as a tool to foster employee involvement in innovation. While previous research has demonstrated the benefits of incorporating game elements in the workplace, the mechanisms and domains through which gamification impacts innovation remain unclear.Design/methodology/approachThis study employed a qualitative approach within the retail sector, conducting semi-structured interviews with Innovation and R&D managers to obtain nuanced insights into the application of gamification in practice.FindingsThe findings indicate that even when applied through short-term or one-off initiatives, gamification generates hedonic, social, and utilitarian outcomes. The employees' sense of ownership of projects and career progression were particularly relevant outcomes. Key factors for successful implementation included a structured and continuous process, transparent metrics to measure impact, alignment with company culture, and leadership support.Originality/valueThis study identifies a comprehensive range of hedonic, social, and utilitarian outcomes of gamification in the open innovation context, as well as the conditions that enable it to foster employee involvement.

Abstract
This paper presents a game-simulated environment that mimics real-world conditions, with a focus on autonomous vehicle navigation. Despite significant advances in the field of games and simulations, there are still a number of challenges to overcome, in particular, the ability to accurately transfer what has been learned in virtual environments to the real world. This project recreates an agent (a motorcycle), modeled with complex physics, navigating autonomously on a detailed map based on the urban geography of Vila Real, Portugal, recreated from real data, implemented in the Unity game engine. In this paper, we provide a detailed overview of the environment and agent creation processes, highlighting the integration of realistic road networks, obstacles, and interaction mechanics that enhance the fidelity of the simulation. The experimental phase demonstrates the motorcycles ability to navigate efficiently, adapting to road layouts, avoiding obstacles, and adjusting to dynamic conditions. The insights from this study can be applied and transferred to real-world application scenarios, particularly in optimizing route planning and driving behaviour for electric motorcycles.

Abstract
Most machine learning (ML) models are not intrinsically well calibrated, meaning that their confidence scores are not consistent with posterior probabilities. Moreover, the most commonly used metric to evaluate calibration, namely the Expected Calibration Error (ECE), has several limitations, including its dependence on hyperparameters such as the number of bins. The ECE estimates local accuracy and confidence by binning predictions in the confidence space. However, this yields a discontinuous estimate and introduces nonlocal effects, since imbalanced data can significantly affect the distribution within bins. To address these issues, we propose the Doubly Kernelized Expected Calibration Error (k2ECE), which introduces kernel based estimates of local accuracy and local confidence that are both centered at each observation and continuous. In addition, we propose a loss function based on this metric. Our results show that jointly optimizing for accuracy and calibration can be a viable approach, particularly when using bilevel optimization, although a trade off between accuracy and calibration is observed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.

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.

Abstract
This chapter explores the potential of thermal energy storage (TES) systems towards the decarbonization of industry and energy networks, considering its coordinated management with electrochemical energy storage and renewable energy sources (RES). It covers various TES technologies, including sensible heat storage (SHS), latent heat storage (LHS), and thermochemical energy storage (TCS), each offering unique benefits and facing specific challenges. The integration of TES into industrial parks is highlighted, showing how these systems can optimize energy manage-ment and reduce reliance on external sources. A district heating use case also demonstrates the economic and environmental advantages of a multi-energy management strategy over single-energy approaches. Overall, TES technologies are presented as a promising pathway to greater energy effi-ciency and sustainability in industrial processes.

Abstract
Handling imbalanced target distributions in regression poses a persistent challenge, as the underrepresentation of relevant target values can significantly hinder model performance. Existing data-level solutions often adapt classification-oriented techniques, introducing arbitrary thresholds over the continuous target and leading to artificial and potentially misleading problem formulations. Deep generative models offer flexible sample synthesis but are computationally intensive and difficult to interpret. We propose a CART-based synthetic sampling method specifically designed for imbalanced regression on tabular data. The method integrates relevance- and density-guided sampling to address sparse target regions without thresholding, and employs a feature-driven tree structure to generate realistic tabular samples across heterogeneous features and non-linear interactions. Experiments on benchmark datasets for extreme-value prediction show that the proposed approach is competitive with state-of-the-art resampling and generative methods while offering faster execution and greater transparency. These results highlight its potential as a scalable and interpretable data-level strategy for improving regression models in imbalanced domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.