Nguyen Hai

Marxist-Leninist philosophy is a mandatory subject in the higher education curriculum in Vietnam. Philosophy aims to build the most basic knowledge bases of human thinking, which are worldview and methodology. Therefore, learning and teaching this subject must have close coordination between professional knowledge and teaching methods. In this article, the author offers a number of measures to develop problem-solving capacity in teaching the Marxist-Leninist philosophy module for students at School of Foreign Languages - Thai Nguyen University from actually teaching the subject. Keywords: Competence, problem solving, students, Marxist-Leninist philosophy. Title: SOME MEASURES TO DEVELOP PROBLEM SOLVING CAPACITY IN TEACHING THE MODULE ON MARXIST-Leninist Philosophy FOR STUDENTS AT SCHOOL OF FOREIGN LANGUAGES - THAI NGUYEN UNIVERSITY Author: Nguyen Thi Hong Hai International Journal of Interdisciplinary Research and Innovations ISSN 2348-1218 (print), ISSN 2348-1226 (online) Vol. 12, Issue 2, April 2024 - June 2024 Page No: 1-5 Research Publish Journals Website: www.researchpublish.com Published Date: 15-April-2024 DOI: https://doi.org/10.5281/zenodo.10972770 Paper Download Link (Source) https://www.researchpublish.com/papers/some-measures-to-develop-problem-solving-capacity-in-teaching-the-module-on-marxist-leninist-philosophy-for-students-at-school-of-foreign-languages---thai-nguyen-university.

The implementation of an artificial neural network for predicting induction heating region locations is proposed in this research. Steel plate deformations during the induction heating process are produced using an analytical solution derived from electromagnetic and plate theory. The plate transform following vertical displacements in each divided area was used as input of neural following desired shape of the steel plate and the specified heating areas for induction treatment as output parameters to predict and evaluate the model. A dataset used 90% for training and remaining 10% for testing to implement on the efficient models when changing hidden layer and its neurons relatively. The trial and error for analyzing and predicting heating-affected regions with the ANNs model reached the high average accuracy and lowest mean square error at 98.08% and 0.00913, respectively. Consequently, the feasibility test indicates that the developed approach may be well utilized to identify the heating positions by grid area in order to achieve the desired plate deformation. Moreover, the analysis of vertical displacement during induction heating and its response behaviour of steel plate based on thermo-mechanical are also addressed.