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

Abstract
Despite the recognised potential of horizontal collaboration in logistics to reduce inefficiencies, and the increasing academic interest in this topic, in practice many initiatives fail. One of the main reasons for this failure is the poor strategy planning and governance organisation. This paper addresses this gap proposing a comprehensive conceptual framework to support the design and implementation of a common strategy for the stakeholders of such partnerships. The research employs qualitative methods, drawing on interviews and the case analysis of existent initiatives. The proposed framework involves the main phases of the strategic formulation, deciding the stakeholder engagement, strategic formulation, operational implementation, and business model elaboration. It serves as a road map for stakeholders to avoid common mistakes and accelerate the deployment of cooperative partnerships.

Abstract
The stochastic dynamic inventory-routing problem (SDIRP) is a fundamental problem within supply chain operations that integrates inventory management and vehicle routing while handling the stochastic and dynamic nature of exogenous factors unveiled over time, such as customer demands, inventory supply and travel times. While practical applications require dynamic and stochastic decision-making, research in this field has only recently experienced significant growth, with most inventory-routing literature focusing on static variants. This paper reviews the current state of research on SDIRPs, identifying critical gaps and highlighting emerging trends in problem settings and decision policies. We extend the existing inventory-routing taxonomies by incorporating additional problem characteristics to better align models with real-world contexts. As a result, we highlight the need to account for further sources of uncertainty, multiple-supplier networks, perishability, multiple objectives, and pickup and delivery operations. We further categorize each study based on its policy design, investigating how different problem aspects shape decision policies. To conclude, we emphasize that large-scale and real-time problems require more attention and can benefit from decomposition approaches and learning-based methods.

Abstract
This study explores the application of multiple predictive algorithms under general versus route-specialized modeling strategies to estimate passenger boarding demand in public bus transportation systems. Accurate estimation of boarding patterns is essential for optimizing service planning, improving passenger comfort, and enhancing operational efficiency. This research evaluates a range of predictive models to identify the most effective techniques for forecasting demand across different routes and times. Two modeling strategies were implemented: a generalistic approach and a specialized one. The latter was designed to capture route-specific characteristics and variability. A real-world case study from a medium-sized metropolitan region in Brazil was used to assess model performance. Results indicate that ensemble-tree-based models, particularly XGBoost, achieved the highest accuracy and robustness in handling nonlinear relationships and complex interactions within the data. Compared to the generalistic approach, the specialized approach demonstrated superior adaptability and precision, making it especially suitable for long-term and strategic planning applications. It reduced the average RMSE by 19.46% (from 13.84 to 11.15) and the MAE by 17.36% (from 9.60 to 7.93), while increasing the average R2 from 0.289 to 0.344. However, these gains came with higher computational demands and mean Forecast Bias (from 0.002 to 0.560), indicating a need for bias correction before operational deployment. The findings highlight the practical value of predictive modeling for transit authorities, enabling data-driven decision making in fleet allocation, route planning, and service frequency adjustment. Moreover, accurate demand forecasting contributes to cost reduction, improved passenger satisfaction, and environmental sustainability through optimized operations.

Abstract
Background: This study aimed to validate the content of a game focused on clinical supervision in nursing, with the collaboration of experts, and to assess its usability alongside a group of nurses. The development of SUPERVISE (R) was grounded in theories of Experiential Learning, Self-Determination, Constructivist, and Social Cognitive.Methods: A mixed study design was used. In the first phase, the content of the game was validated with the participation of experts using a modified e-Delphi method. In the second phase, the usability of SUPERVISE (R) was tested with nurses.Results: In the first phase, the content of the game was validated by 36 experts, reaching a consensus = 95.4% on the 128 questions on which the game was based. In the second phase, the SUPERVISE (R) game was tested and evaluated by 39 nurses. It showed good usability and with a System Usability Scale score = 79.4 (above the cut-off of 68) and was recognised as an effective teaching strategy.Conclusion: This study highlights the importance of combining rigorous content validation with practical evaluation to develop effective gamified educational tools for nursing practice.

Abstract
Acoustic Event Detection and Classification (AEDC) aims to identify and classify specific audio events within audio signals. AEDC has applications in various fields, including security systems, scene monitoring, smart hospitals, environmental monitoring, and more. The process of AEDC typically involves steps that include audio signal processing to extract relevant features from the input, a machine learning model to recognise patterns in the extracted features and a classifier to detect events. Recent research on AEDC has increasingly focused on features based on the frequency distribution of the Mel-frequency cepstral coefficients (MFCCs). In this study, the feature extraction is performed based on Cochleogram, which involves the analysis of audio signals using Gammatone filters. Cochleogram features are inspired by the human cochlea, part of the inner ear responsible for converting sound vibrations into electrical signals sent to the brain. A two-dimensional (2D) feature is extracted from the Cochleogram using Welch’s spectral density estimation and then converted into a frequency spectrum. The frequency distribution of different cochleogram filter banks is then used as a one-dimensional (1D) feature. The proposed classification method uses a 1D Convolutional Neural Network (CNN), which is less complex than traditional 2D CNNs. The proposed method was evaluated using the URBAN-SED dataset, and its performance was compared against the related state-of-the-art methods. The results showed the competitiveness of the cochleogram over Mel-based features such as MFCC in AEDC if the deep learning algorithm is properly designed and trained. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.