Problem Based Learning A Didactic Strategy In The Teaching Of System Simulation
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Problem-Based Learning: A Didactic Strategy in the Teaching of System Simulation
This book describes and outlines the theoretical foundations of system simulation in teaching, and as a practical contribution to teaching-and-learning models. It presents various methodologies used in teaching, the goal being to solve real-life problems by creating simulation models and probability distributions that allow correlations to be drawn between a real model and a simulated model. Moreover, the book demonstrates the role of simulation in decision-making processes connected to teaching and learning.
Neutrosophic Sets and Systems, vol. 74/2024 {Special Issue: Advances in SuperHyperStructures and Applied Neutrosophic Theories}
Author: Florentin Smarandache
language: en
Publisher: Infinite Study
Release Date: 2024-12-16
This volume contains the proceedings of the conference held at the University of Guayaquil on November 28 and 29, 2024, featuring contributions from researchers representing Colombia, Cuba, Ecuador, Spain, the United States, Greece, Japan, Mexico, and Peru. The conference focused on SuperHyperStructures and Applied Neutrosophic Theories, commemorating the 30th anniversary of neutrosophic theories and their extensive applications. The topic of SuperHyperStructures and Neutrosophic SuperHyperStructures explores advanced mathematical frameworks built on powersets of a set 𝐻, extending to higher orders 𝑃𝑛(𝐻). SuperHyperStructures are constructed using all non-empty subsets of 𝐻, while Neutrosophic SuperHyperStructures incorporate the empty set 𝜙, representing indeterminacy. These structures model real-world systems where elements are organized hierarchically, from sets to sub-sets and beyond, enabling the analysis of complex and indeterminate relationships.
Convergence of Deep Learning and Internet of Things: Computing and Technology
Digital technology has enabled a number of internet-enabled devices that generate huge volumes of data from different systems. This large amount of heterogeneous data requires efficient data collection, processing, and analytical methods. Deep Learning is one of the latest efficient and feasible solutions that enable smart devices to function independently with a decision-making support system. Convergence of Deep Learning and Internet of Things: Computing and Technology contributes to technology and methodology perspectives in the incorporation of deep learning approaches in solving a wide range of issues in the IoT domain to identify, optimize, predict, forecast, and control emerging IoT systems. Covering topics such as data quality, edge computing, and attach detection and prediction, this premier reference source is a comprehensive resource for electricians, communications specialists, mechanical engineers, civil engineers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.