Abstract
Education 5.0 is a new paradigm in education posing many challenges and opportunities. This paper uses qualitative methods to explore students' and teachers' experiences with online learning to understand the challenges, benefits, and vision for a successful blended learning model, proposing a dynamic framework for blended learning. Results of in-depth interviews show the three main challenges of blended learning: pedagogical design, technological design, and environment/ setup design. Finally, the study discusses insights into future directions for developing Education 5.0, including the need for ongoing research, collaboration communities, curricula personalization, and innovation in the field.

Abstract
Time series forecasting is pivotal across industries, as it fosters data-driven decision-making, increasing the chances of successful outcomes. Yet, certain instances that feature adverse characteristics, may lead models to manifest stress through decreases in performance (e.g., large errors). Hence, the ability to preemptively identify such cases, while establishing their root causes, would be advantageous to elevate the understanding of forecasting processes, informing users about the trustworthiness of predictions. Hence, we propose MASTFM, a method based on meta-learning that leverages statistical characteristics of input time series, and estimations of forecasting performance from model outputs, to build a metamodel that learns conditions for stress. Given that such occurrences are naturally rare, data augmentation is employed to ensure balance during training. Moreover, SHapley Additive exPlanations (SHAP) are used to explain how features impact forecasting behaviour.

Abstract
[No abstract available]

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.