Due to a complex of factors, visually impaired people are facing difficulties and increased risks during fire emergencies and evacuations from different types of buildings. Even if a lot of studies have been conducted to improve the mobility and autonomy of people with visual impairment during emergency evacuation processes, these offer only partial solutions, especially in the presence of uncertainties characteristic of fire evolution. Aiming for a more comprehensive approach to the safe evacuation of people with visual impairments, this paper proposes a support system that integrates innovative aspects related to the architecture of the application, modeling and simulation methods, and experimental realization. The system is decentralized, capable of anticipating possible fire extensions and determining, in real-time, new corresponding evacuation routes. The overall design complies with the standard norms in emergency situations. Two models, one developed in Stateflow and the other based on Delay Time Petri Nets (DTPN), were constructed to describe the dynamic behavior of the system in the presence of unexpected events that can change the initial recommended evacuation path. To test the functionality and efficiency of the proposed system, the conditions created by potential fire sources were simulated as a part of realistic scenarios. Tests were conducted with visually impaired people. Simulation and prototype testing showed that the presented system can improve evacuation times, achieving a measurable gain compared to scenarios where there is no information regarding fire evolution.

Augmented Reality (AR) is an innovative tool in education, enhancing learning experiences across multiple domains. This literature review explores the application of AR in education, with a particular focus on geographical learning. The study begins by tracing the historical development of AR, distinguishing it from Virtual Reality (VR) and highlighting its advantages in an educational context. The integration of AR into learning environments has been shown to improve engagement, comprehension of abstract concepts, and collaboration among students. The use of AR in geographical education through interactive applications, such as GeoAR and AR Sandbox, improves the exploration of spatial relationships, topographic maps, and environmental changes. Studies demonstrate that AR enhances students’ ability to recall information and understand geographical processes more effectively than with traditional methods. Furthermore, AR Sandbox implementations, including Illuminating Clay, SandScape, and AR Sandbox, are analyzed and compared. The paper also discusses future developments in AR for geography education for AR Sandbox, such as the integration of a mobile application for extended learning and improving computing solutions through Raspberry Pi. These advancements aim to make AR systems more accessible and to increase the benefits to both students and professors.

The emergency evacuation of visually impaired individuals during fire incidents presents critical challenges that require innovative technological solutions. While existing evacuation systems provide static route guidance, they fail to adapt dynamically to evolving fire conditions, blocked passages, or dangerous zones in buildings with multiple routes and exits. This paper presents a comprehensive implementation of a mobile application built with Flutter/Dart that addresses these limitations by enabling real-time, dynamic route computation based on live sensor data. The presented system operates in a decentralized manner, performing all critical computations on-device to ensure its functionality even when some parts of the building infrastructure fail. A dynamic route calculation modified Dijkstra’s algorithm was implemented on each user’s phone for guidance. If initial path adjustments are needed, they are computed from sensor data to evaluate fire evolution and other relevant factors, including the user’s current position and crowd congestion. An audio–visual interface was designed to provide navigation instructions and to help users follow safety routes efficiently. Field testing with visually impaired participants demonstrated significant improvements in evacuation efficiency, with shorter evacuation times than traditional static guidance approaches. The system architecture complies with international fire safety standards while maintaining user privacy through a no-tracking design philosophy. This work contributes to both theoretical advances in adaptive evacuation algorithms and practical insights for deploying assistive technologies in emergency scenarios.

This paper provides the modeling, implementation, and simulation of fractional-order processes associated with the production of the enriched ¹³C isotope due to chemical exchange processes between carbamate and CO₂. To demonstrate and simulate the process most effectively, an execution of a new approximating solution of fractional-order systems is required, which has become possible due to the utilization of advanced AI methods. As the separation process exhibits extremely strong nonlinearity and fractional-order-based performance, it was similarly necessary to utilize the fractional-order system theory to mathematically model the operation, which consists of the comparison of its output with an integrator function. The learning of the dynamic structure’s parameters of the derived fractional-order model is performed by neural networks, which are AI-based domain solutions. Thanks to the approximations executed, the concentration dynamics of the enriched ¹³C isotope can be simulated and predicted with a high level of precision. The solutions’ effectiveness is corroborated by the model’s response comparison with the reaction of the actual process. The current implementation uses neural networks trained specifically for this purpose. Furthermore, since the isotopic separation processes are long-settling-time processes, this paper proposes some control strategies that are developed for the ¹³C isotopic separation process, in order to improve the system performances and to avoid the loss of enriched product. The adaptive controllers were tuned by imposing them to follow the output of a first-order-type transfer function, using a PI or a PID controller. Finally, the paper confirms that AI solutions can successfully support the system throughout a range of responses, which paves the way for an efficient design of the automatic control for the ¹³C isotope concentration. Such systems can similarly be implemented in other industrial processes.