PriMera Scientific Engineering (ISSN: 2834-2550) - Article In Press

The trajectories of educational technology and artificial intelligence, once parallel and largely independent, have now converged so completely that treating them as separate domains no longer makes sense. In the early 2000s, a master’s degree in educational technology concerned itself with multimedia authoring, learning-management systems, web-based resources and generic evaluation models. Meanwhile, AI labs were refining neural networks, speech recognition, computer vision and, eventually, the large language models that power today’s conversational agents. These two strands have now braided into a single rope that pulls the entire education sector toward an intelligent, data-saturated future. Adaptive tutoring engines adjust reading passages on the fly, chatbots scaffold metacognitive reflection, multimodal analytics transform clickstreams into personalised intervention plans, and extended-reality headsets gather psychomotor data in virtual laboratories. A postgraduate curriculum that still majors on yesterday’s toolkits risks preparing graduates for a world that no longer exists.

The objective of this study was to study chemical kinetics of transected biodiesel production under high voltage electric field. Experimental investigation was carried out with methanol and Tung oil at molar ratio of 8:1 with 20 kV power supply for high voltage electric field. The starting of glycerin separation occurred within 1 min after plasma field generation and the process finished after 10 minutes for 100 ml of Tung oil of which the obtained biodiesel was 97 ml.
Study on chemical kinetics of biodiesel production, it was found that apparent activation energy was about 34.97 kJ/mol. The reaction rate equation was best approximated with a pre-exponential factor of 284.18 s^-1.
Keywords: Biodiesel; Effects of electrode configuration; Electric Field Technique

This paper presents the FLUENT model, Flexible Universal Education Model for a New Hybrid Teaching, a framework for university course planning developed within the Erasmus+ project through a design-based research approach. Rooted in empirical evidence from systematic literature reviews (PRISMA), stakeholder interviews, and cross-national surveys, the model addresses the growing complexity of hybrid education by promoting flexibility, personalization, interaction, collaboration, and adaptability across both face-to-face and online contexts. Underpinned by constructivist and cognitive psychology theories, FLUENT views learning as an active process of knowledge construction, emphasizing competency-based learning and structured learning paths. Its key structural elements - time, space, technology, student agency, and teacher reflectivity - guide the design of reversible, inclusive, and context-sensitive learning environments. A core innovation is the model’s online-first perspective, which ensures readiness for shifting teaching modalities and diverse learner needs. Accompanying the model is the FLUENT curriculum and a modular online course (launching Winter 2024/25), offering practical implementation tools for educators. Developed collaboratively by institutions across four European countries, FLUENT offers a scalable, research-informed approach to hybrid pedagogy - equipping faculty to design engaging, student-centred courses and respond effectively to contemporary challenges in higher education.
Keywords: Hybrid Teaching; Design-Based Research; Course Planning; Flexible Learning; Student Agency; Competency-Based Education

The increasing connectivity and complexity of modern systems, from Industrial Control Systems (ICS) to Internet of Things (IoT) networks, have dramatically expanded the attack surface for cyber threats. Traditional security frameworks struggle to adapt to the dynamic nature of these environments and often rely on static perimeter-based defenses or rigid policy enforcement. This study introduces the Cognitive Zero-Trust Resilience Framework (CZTRF), a novel approach that integrates cognitive computing capabilities with zero-trust architectural principles to create a self-evolving security posture. Unlike conventional frameworks, CZTRF continuously analyzes the operational environment, threat intelligence, and system vulnerabilities to dynamically adjust security controls. By leveraging machine learning to refine the Integrated Cyber Risk Management (ICRM) equation variables in real time, the framework quantifies risk more accurately and automatically implements proportional countermeasures. CZTRF extends beyond traditional zero-trust by incorporating resilience metrics that measure a system's ability to prepare for, absorb, recover from, and adapt to adverse cyber events. The simulation results demonstrate that CZTRF significantly improves threat prediction accuracy, reduces response time, and enhances system resilience compared with static security frameworks. This study presents the theoretical foundations, architectural components, implementation methodology, and validation results of the CZTRF, offering a comprehensive approach to securing increasingly complex connected systems against evolving cyber threats.
Keywords: adaptive cybersecurity; cognitive computing; connected systems; machine learning, OT/ICS security; quantitative risk assessment; resilience; zero-trust architecture