This document is in the required format. This work shows a benchmark of e-Learning tools including an approach for comparing them based on histogram specification concepts. The analysis is based on the definition of a set of criteria which are useful and desirable characteristics of learning management systems. The final results show the evaluation from different views including the approach based on their histograms. The evaluation of each e-Learning tool is based on the use of a three-dimensional model which organizes the criteria in three different axes according to their functionality inside the model, namely: Management, Technological and Instructional. With the application of the evaluation methodology we can assess the tools from different points of view. One of the main objectives of this work is to help users and developers of e-Learning tools to make good decisions about which tool have the best features for developing training and learning systems and for development and management of resources, courses and learning objects.
The aim of this work is to present a model for heat transfer, desorbed refrigerant, and pressure of an intermittent solar cooling system’s thermochemical reactor based on backpropagation neural networks and mathematical symmetry groups. In order to achieve this, a reactor was designed and built based on the reaction of BaCl2-NH3. Experimental data from this reactor were collected, where barium chloride was used as a solid absorbent and ammonia as a refrigerant. The neural network was trained using the Levenberg–Marquardt algorithm. The correlation coefficient between experimental data and data simulated by the neural network was r = 0.9957. In the neural network’s sensitivity analysis, it was found that the inputs, reactor’s heating temperature and sorption time, influence neural network’s learning by 35% and 20%, respectively. It was also found that, by applying permutations to experimental data and using multibase mathematical symmetry groups, the neural network training algorithm converges faster.
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