Optimizing University Mobility : An Internal Navigation and Crowd Management System

In the evolving landscape of educational technology, the article explores the critical frontier of indoor navigation systems, focusing on universities. Traditional approaches in higher education often fall short of meeting dynamic user expectations, necessitating revolutionary solutions. This research introduces an innovative internal navigation and crowd management system that seamlessly integrates augmented reality, natural language processing, machine learning, and image processing technologies. The Android platform serves as the foundation, harnessing augmented reality's transformative capabilities to provide real-time visual cues and personalized wayfinding experiences. The voice interaction module, backed by NLP and ML, creates an intelligent, context-aware assistant. The crowd management module, employing advanced image processing, delivers real-time crowd density insights. Personalized recommendations, powered by NLP and ML, offer tailored canteen suggestions based on user preferences. The agmented reality navigation module, using Mapbox, Unity Hub, AR Core, and Vuforia, enriches the user experience with dynamic visual cues. Results reveal the success of each module: the voice interaction module showcases continuous learning, user-centric feedback, contextual guidance excellence, robust security, and multimodal interaction flexibility. The crowd management module excels in video feed processing, image processing with OpenCV, and real-time availability information retrieval. The personalized recommendations module demonstrates high accuracy, equilibrium, and robust performance. The AR navigation module impresses with precision, enriched navigation, and tailored routes through machine learning. This cohesive system sets new benchmarks for user-centric technology in universities. Future work includes multi-university integration, intelligent spatial design, and real-time decision support, paving the way for more efficient, user-centered university experiences and contributing to the advancement of smart university environments. The research serves as a pivotal force in reshaping interactions within university spaces, envisioning a future where technology seamlessly enhances the essence of human interaction in educational environments

Learning Management System Built Using the MERN Stack

Web based applications play a main role in our day-to-day life and therefore, it is important to ensure the quality and reliability of web applications. With the sudden increase in use of web based applications for online and distant learning, it is important to address existing issues in current Learning Management Systems (LMS), so that users can benefit from a better, uninterrupted learning experience. This work mainly contributes to understanding how the MERN stack can be efficiently used in building a reliable and secure LMS that will provide its services free of charge so that students are provided with a free and uninterrupted learning experience. An LMS that is equipped with user handling functionality, managing courses and course materials, and maintaining a library, has been the focus of the study that resulted in producing this paper. The work also includes reasoning as to why the MERN stack was selected for developing the proposed system

Nuclear magnetic moment of

The nuclear magnetic moment of the ground state of 57Cu(Iπ = 3/2-, T1/2 = 196.3 ms) has been measured to be |μ(57Cu)| = (2.00 ±0.05) μN using the β-NMR technique. Together with the known magnetic moment of the mirror partner 57Ni, the spin expectation value was extracted as $\langle\sum\sigma_z\rangle$ = -0.78 ± 0.13. Discrepancy between present results and shell model calculations in the full fp shell implies significant shell breaking at 56Ni with the neutron number N = 28