Athenea Engineering sciences journal

Use of Maple in the teaching of physics in engineering

2024-12-21T01:56:04+00:00

This paper presents a theoretical analysis of the use of new technologies in teaching physics in engineering careers. Special emphasis is made using Maple software, since it is an easy-to-use and free-access tool, which allows students to learn quickly and efficiently, besides providing potential graphic resources. The literature is reviewed and the main results and proposals for academic development that can favor physics teaching in engineering and technical careers are presented. The results reveal that technology in education is a significant alternative to creating spaces for group interaction and learning, which includes computational skills as a valuable resource for the new professional.

Genesis, lithology and mineralogy iron ore deposits with high-grade metamorphism

2024-12-21T01:55:54+00:00

This study presents a documentary investigation of the genesis of the San Isidro Ferrous Quadrilateral, located in the Bolivar State, Venezuela, with emphasis on Los Barrancos mine, describing the origin of the ferruginous quartzites and the formation by metamorphism of the iron-ore lithologies. The methodology for the evaluation of these ores was composed of a mineralogical characterization, a chemical analysis and a macroscopic inspection, to then classify them by their mineralogy and texture. The main results show that it was possible to define the genesis, lithology and mineralogy of this ferrous deposit.

Use of artificial intelligence in medical classification for hemiplegic patients

2024-12-21T01:55:44+00:00

This study explores a machine learning-based neural network system that uses MATLAB to classify hemiplegia, a disease that causes paralysis on one side of the body. An algorithm was developed to categorize patients into four established types of hemiplegia. Techniques such as Principal Component Analysis (PCA) and Self-Organizing Maps (SOMs) were used for dimensionality reduction and data clustering, while a Convolutional Neural Network (CNN) refined the classification. The algorithm identified distinct subgroups within the categories, indicating a more complex data structure. Despite promising results to aid clinical diagnosis, further exploration of these subcategories is needed.