Abstract
This paper introduces PRISMA, a hybrid multi-agent Deep Reinforcement Learning (DRL) framework for solving the Flexible Job-shop Scheduling Problem (FJSP). It uses Constraint Programming (CP) solutions to pretrain decentralized policies and to guide exploration during training. Although DRL can generate fast solutions for large combinatorial problems, it often fails to match the quality of optimization methods, motivating the integration with hybrid frameworks. The growing interest in embedding domain knowledge into learning algorithms has produced several hybrid formulations, yet their potential remains underexplored, particularly in multi-agent settings. PRISMA combines supervised and reinforcement learning within a multi-agent framework, where CP solutions are used to (i) learn expert decisions through imitation learning, and (ii) train an auxiliary network that guides DRL training via reward shaping. A shared graph network is adopted for transferring system-level knowledge into machine-level observations, enabling fast and consistent inference from enriched local embed-dings. To the best of our knowledge, PRISMA introduces the first expert-derived guidance mechanism for the FJSP and is among the earliest to apply imitation learning within a multi-agent formulation. By combining both modules, it strengthens the bridge between optimization and learning-based methods, where such dual integrations remain scarce. Experimental results show faster convergence and higher solution quality than state-ofthe-art DRL models. PRISMA achieves an average optimality gap of 6.74%, corresponding to a 50% relative improvement over the single-agent baseline, while reducing inference time. These findings reinforce the value of merging optimization accuracy with the flexibility of multi-agent DRL for efficient scheduling.

Abstract
Real-world datasets used in data-driven modelling are often affected by inconsistencies arising from discrepancies between recorded observations and actual system behaviour. Conventional approaches to data filtering and quality assessment rely primarily on statistical criteria and treat consistency as an intrinsic property of the dataset. However, such approaches may fail to identify observations that are statistically plausible yet incompatible with the structural assumptions underlying the model.This work introduces a model-dependent perspective on data consistency, in which the validity of observations is defined relative to the model used to interpret the data rather than to distributional properties alone. Within this framework, residuals are interpreted not merely as noise, but as indicators of incompatibility between observed data and model-defined behaviour.Importantly, inconsistencies may arise not only from anomalies in the target variable, but also from incorrect, incomplete, or misaligned representations of explanatory variables, even when observed outputs remain statistically valid. By formalising data consistency as a model-dependent property, this work challenges the conventional separation between data preprocessing and modelling, and reframes data filtering as part of the interpretation of model-data relations.The proposed framework provides a conceptual basis for integrating data consistency into data-driven modelling processes, with implications for data interpretation, representation, and validation in systems operating under imperfect real-world data conditions.

Abstract
This article draws upon a professional development course intended to promote innovation in higher education through generative technologies. The course aimed to assist university educators in developing both technical and critical forms of understanding related to emerging AI tools. Advanced AI generative technologies and its applications in pedagogy was the central theme of the course. It incorporated machine learning basics, natural language processing, prompt designing, and content creation across media. Particular attention was paid to how these technologies could improve learning through tailored information delivery, personalized interactive feedback, and creativity in teaching. Apart from the technical side, participants grappling with concepts of educational AI were encouraged to explore social and ethical issues, which guided responsible examination of the use of AI within educational contexts. Collaborative work, case discussion, and individual reflection were used as the main activities. Participants used ChatGPT, Gemini, and Copilot for lesson planning, resource creation, and student participation. In their final reflections, participants reported increased confidence in employing digital strategies, openness to novel pedagogical frameworks, and an emerging understanding of the implications of artificial intelligence in educational settings. The results indicate that, when adopted with careful consideration, generative technologies have the potential to positively contribute to pedagogical practices in higher education. Instead of supplanting current practices, they provide additional avenues for facilitating learning, nurturing creativity, and addressing multifaceted student needs. This initiative adds to the continuing conversations on the evolution of pedagogy and andragogy in relation to technology.

Abstract
This study applies phase plane analysis of medio-lateral, anteroposterior, and vertical directions for the coordination assessment of whole-body (WB) center of mass (COM) movement during the impulse phase of a standard maximum vertical jump (MVJ) with long, short, and no countermovement (CM). A video system and force platform were used, with the amplitudes of WB COM excursion obtained from image-based motion capture at each anatomical direction, and the 2D and 3D mean radial distance were compared under long, short, and no CM conditions. The estimate of the population mean length was used as a measure of distribution concentration, and the Rayleigh statistical test for circular data was applied with the sample distribution critical value. Watson's U2 goodness-of-fit test for the von Mises distribution was used with the mean direction and concentration factor. The applied metrics led to the detection of shared and specific features in the global and phase plane analysis of WB COM movement coordination in the medio-lateral, anteroposterior, and vertical directions during long, short, and no CM conditions in relation to MVJ performance assessed from ground reaction force (GRF) through the force platform. Thus, long, short, and no CM impulses share lower amplitudes of WB COM excursion in the medio-lateral direction and mean radial distance to its mean, whereas the anteroposterior and vertical excursion of WB COM, along with the 2D transversal and 3D spatial length of the WB COM path, present as potential predictors of MVJ performance, with distinct behavior in long CM compared to short and no CM. Additionally, the applied workflow on generalized phase plane analysis led to the detection, through complementary metrics, of the anatomical WB COM movement directions with higher coordination based on phase concentration tests at 5% significance, in line with MVJ performance under different CM conditions.

Abstract
Irrigation systems are a major source of energy consumption in agriculture, primarily due to water pumping. Increasing electricity price variability and renewable integration make energy-efficient irrigation operation challenging.This work proposes an energy-aware irrigation scheduling framework that combines predictive energy modelling with tariff-based optimisation. Rather than relying on explicit price forecasts, the approach uses relative cost representations derived from time-of-use tariffs, enabling robust scheduling under uncertain pricing conditions.Energy consumption is modelled as a function of aggregated system operation and embedded within a mixed-integer optimisation framework that determines irrigation schedules while satisfying agronomic, hydraulic, and operational constraints, including photovoltaic (PV) integration.The framework is evaluated using real-world data from a vineyard and an olive grove under varying demand, tariff, PV, and flexibility conditions. Results show substantial cost reductions without modifying irrigation demand. Benefits increase with tariff differentiation and system flexibility, while PV integration provides additional gains. In more flexible systems, scheduling may also reduce total energy consumption.Overall, irrigation scheduling emerges as an effective demand-side flexibility mechanism, improving both economic performance and energy efficiency.

Abstract
Periods of crisis often accelerate digital transformation in higher education institutions, forcing the rapid adoption of educational technologies such as learning management systems, digital assessment platforms, and video conferencing tools. While existing research has extensively examined emergency responses, less attention has been given to how these technologies evolve from short-term solutions into sustained institutional infrastructures that support long-term organizational learning and digital capability development. This study investigates how educational technologies were mobilized and selectively institutionalized during a crisis-driven disruption in a higher education institution. Using a longitudinal institutional case study based on operational platform data, assessment records, support service logs, and institutional documentation, the study analyzes patterns of resistance (limited digital embedding), accelerated adoption, and post-crisis consolidation of digitally mediated practices across teaching, assessment, and academic support activities. The findings show that educational technologies evolved from peripheral instructional tools into core institutional infrastructures supporting teaching, assessment, academic coordination, and content management. These processes fostered organizational learning and contributed to the development of sustained digital capabilities and institutional resilience. Based on these findings, the study proposes an empirically grounded Institutional Digital Transformation Model that conceptualizes crisis-driven digital transformation as a phased process linking technology adoption, organizational learning, and institutional resilience. The model offers a conceptual and practical framework for understanding and guiding sustainable digital transformation in higher education institutions.