Resistive Random Access Memories (RRAMs) are based on resistive switching (RS) operation and exhibit a set of technological features that make them ideal candidates for applications related to non-volatile memories, neuromorphic computing and hardware cryptography. For the full industrial development of these devices different simulation tools and compact models are needed in order to allow computer-aided design, both at the device and circuit levels. Most of the different RRAM models presented so far in the literature deal with temperature effects since the physical mechanisms behind RS are thermally activated; therefore, an exhaustive description of these effects is essential. As far as we know, no revision papers on thermal models have been published yet; and that is why we deal with this issue here. Using the heat equation as the starting point, we describe the details of its numerical solution for a conventional RRAM structure and, later on, present models of different complexity to integrate thermal effects in complete compact models that account for the kinetics of the chemical reactions behind resistive switching and the current calculation. In particular, we have accounted for different conductive filament geometries, operation regimes, filament lateral heat losses, the use of several temperatures to characterize each conductive filament, among other issues. A 3D numerical solution of the heat equation within a complete RRAM simulator was also taken into account. A general memristor model is also formulated accounting for temperature as one of the state variables to describe electron device operation. In addition, to widen the view from different perspectives, we deal with a thermal model contextualized within the quantum point contact formalism. In this manner, the temperature can be accounted for the description of quantum effects in the RRAM charge transport mechanisms. Finally, the thermometry of conducting filaments and the corresponding models considering different dielectric materials are tackled in depth.

Educational institutions face growing challenges. Rising enrolment, limited budgets, and sustainability goals demand more efficient resource management and administrative decision-making. To address challenges like these, this work proposes a conceptual framework for smart campus management which integrates Artificial Intelligence (AI) and advanced wireless networks based on 5G. The framework’s design outlines layers for campus data collection (via sensors and connected devices), high-speed communication, and AI-driven analytics for decision support. By leveraging data-driven insights enabled by reliable wireless connectivity, institutions can make more informed decisions, use resources more effectively, and automate routine tasks. Envisioned AI capabilities include forecasting (for predictive maintenance and demand planning), anomaly detection (for fault or irregularity identification), and optimisation (for resource scheduling). Rather than reporting empirical results, the framework is illustrated through hypothetical scenarios (e.g., anticipating equipment maintenance, dynamically scheduling classrooms, or reallocating resources) to present potential benefits and tools for researchers. The discussion also highlights how the framework incorporates data privacy, security, and accessibility considerations to ensure inclusive adoption. Eventually, this conceptual proposal provides a roadmap for administrators and planners, guiding the adoption of AI and wireless innovations in educational management to enable more responsive, efficient governance and, ultimately, improve outcomes for students and staff.

Advances in telecommunications and artificial intelligence (AI) are reshaping modern educational spaces. Drawing upon diverse resources, this systematic literature review examines how these new advances including 5G, Internet of Things (IoT), and AI-based analytics can transform conventional classrooms into adaptive, secure, and highly interactive environments. Real-time data collection and personalized feedback systems are found to significantly enhance engagement and accessibility for diverse learner populations. Furthermore, emerging security architectures, such as zero-trust frameworks and AI-driven intrusion detection, mitigate cyber threats and strengthen data confidentiality. Nevertheless, it is found that broader adoption is limited due to practical hurdles, which include budget allocation, professional development, and regulatory compliance. In response, strategic recommendations are provided to guide the planning and implementation of intelligent telecommunications in different educational contexts while noting the need for responsible data governance and equitable access. By illustrating how AI-assisted connectivity can enhance personalized instruction while safeguarding learner privacy, this study offers a forward-looking perspective on modern pedagogical approaches which can balance technological innovation with ethical considerations.

The two main issues in the area of the Internet of Things are low-resource consumption and secure data transmission. Conjugating both is fairly hard on ensuring security, leading to great efforts in research. The standard cryptography methods currently proposed are based on simplifications of standard protocols, but are still demanding on resources. Chaotic encryption is a way to reduce this burden, while keeping an equivalent level of security. In this article, we propose, for the first time, a purely digital scheme for chaotic secure communications, able to be implemented in hardware or software without occupying most of the available resources. Next to the delivered analysis of the system, the experimental demonstration on both FPGA and ESP32 Arduino platforms of chaotic synchronization between transmitter and receiver, included examples of applied encrypted communication in the case of consecutive picture and text transmissions.