Atman and Brahman are the most fundamental entities of known and unknown to understand it and their relationship with each other can be understood by Mahavaakyas and References of Upanishads like Chandogya, Brihadaranyak, and Mandukya Upanishad. The concept of the relation between Atman and Brahman can be explained using concepts of Natural Science. Metaphysics of correlation of existence and necessity of both entities can be studied using this concept.

Observational learning can enhance the acquisition and performance quality of complex motor skills. While an extensive body of research has focused on the benefits of synchronous (i.e., concurrent physical practice) and non-synchronous (i.e., delayed physical practice) observational learning strategies, the question remains as to whether these approaches differentially influence performance outcomes. Accordingly, we investigate the differential outcomes of synchronous and non-synchronous observational training contexts using a novel dance sequence. Using multidimensional cross-recurrence quantification analysis, movement time-series were recorded for novice dancers (...) who either synchronised with (n = 22) or observed and then imitated (n = 20) an expert dancer. Participants performed a 16-count choreographed dance sequence for 20 trials assisted by the expert, followed by one final, unassisted performance trial. Although end-state performance did not significantly differ between synchronous and non-synchronous learners, a significant decline in performance quality from imitation to independent replication was shown for synchronous learners. A non-significant positive trend in performance accuracy was shown for non-synchronous learners. For all participants, better imitative performance across training trials led to better end-state performance, but only for the accuracy (and not timing) of movement reproduction. Collectively, the results suggest that synchronous learners came to rely on a realtime mapping process between visual input from the expert and their own visual and proprioceptive intrinsic feedback, to the detriment of learning. Thus, the act of synchronising alone does not ensure an appropriate training context for advanced sequence learning. (shrink)

Accurately predicting store sales is essential for businesses to optimize inventory management, marketing strategies, and staffing. Traditional sales prediction models often rely on historical data and simple linear trends, but these methods can be limited in capturing the complexity of factors that affect sales. This paper explores the application of machine learning (ML) algorithms to predict store sales, considering factors like promotions, holidays, weather conditions, and seasonal trends. We analyze various machine learning models, evaluate their performance, and demonstrate how they (...) can be used to improve sales forecasting and operational efficiency. (shrink)

The increasing demand for secure and efficient voting systems has led to the exploration of online voting solutions. Traditional voting methods are often vulnerable to fraud, inefficiencies, and logistical challenges. This paper presents an online voting system that leverages machine learning techniques to enhance security, accuracy, and accessibility. The system employs facial recognition for voter authentication, anomaly detection to prevent fraudulent activities, and natural language processing (NLP) for user interaction. Experimental results indicate that the proposed model provides a reliable, tamper-resistant, (...) and user-friendly voting experience. (shrink)