The use of virtual reality (VR) has increased in many different areas, especially in the learning process. One of the most important VR tools is zSpace platform, which can be used as a virtual learning environment. This paper aims to increase the awareness of applying VR technology in Saudi Arabia through using zSpace as a virtual learning environment in all Saudi schools rather than using traditional learning methods. As a case study, we chose Al-Hamra School in Jeddah. By analyzing tests and questionnaires with students and teachers, we examined the effect of using zSpace on students’ grades, educational achievement, and learning skills. We have demonstrated that using zSpace instead of traditional learning methods in the learning process had better effect students, which is considered to be a largely positive effect in the learning environment.

The Rivest-Shamir-Adleman (RSA) cryptosystem is an efficient and secure method for transmitting data over the Internet. Breaking this system primarily relies on the integer factorization problem, which involves factoring a composite odd number, n, into two prime factors, p and q. Euler-based Fermat Factorization (EFF) is one of the factoring methods based on the modular multiplication operation and is efficient when δ = p − q ≤ n0.25. However, the execution times of the EFF algorithm increase significantly with large values of n and δ > n0.25. In this paper, we propose a novel technique for optimizing the number of modular multiplication operations required to find the two factors. The method can factor a large odd number in a fast time, even when δ > n0.25. For different values of the length of the primes and δ, the experimental results indicate that the proposed algorithm is, on average, 85.5% faster than the previous improvements on the Fermat factorization method.

We present a novel weather analysis of San Francisco from 2009 to 2019 using concepts from quantum information theory. We describe how to transform a classical weather dataset into a quantum-inspired analytical framework by treating it as a quantum information system embedded in a quantum-like representation, with data encoded as density matrices in Hilbert space. Transforming classical weather data into quantum states is a mathematical abstraction, whereas a quantum-inspired system is not a quantum-physical system. We study a quantum-inspired analytical framework using quantum information-theoretic metrics, including fidelity, classical information, quantum information, decoherent information, Shannon and von Neumann entropies, and the quantum stability index, to represent and analyze the dataset. Furthermore, we give predictions for each quantum information metric for 2020 and 2021. We fit Fourier-series models to the metrics to explore temporal patterns and structural properties in the data.