The National Survey on Drug, Alcohol and Tobacco, 2016-2017, notes that 15.6 million Mexicans are active smokers and, by 2030, expect the death of 8 million cancers of the larynx or lung. Therefore, the World Health Organization (WHO) recommends detecting precancerous lesions of the larynx. This is possible, as they are characterized by a biomarker pattern manifested by the alteration of the biomechanical interpretation of the vocal cords, regardless of the sex and age of the smoker. The goal of this article is to evaluate three machine learning methods: neural networks, Gaussian networks, and decision tree to determine the method that best solves the problem of detecting patterns of precancerous vocal cord injury biomarkers. It uses the WEKA tool and a knowledge bank, endorsed by NOM-012-SSA3-2012, with 250 patterns, and provided by the Luis Guillermo Ibarra National Institute of Rehabilitation, Ibarra. The performance of the methods was compared by ROC curves and confusion matrices, under the criteria established by ISO-5725. The decision tree the method that best responds to the detection of patterns of biomarkers of precancerous lesions of the vocal cords.
La invención y las mujeres en Mexico. Handbooks-©ECORFAN-Mexico, Querétaro, 2019.
Compression fractures in the lumbar region are usually caused by excessive pressure at the level of the vertebral body. The fracture occurs when the vertebral body is crushed, causing the anterior part of the vertebral body to acquire a wedge shape. Bone tissue inside the vertebral body is crushed or compressed. Compression fractures due to trauma may be due to a fall, a strong jump, a car accident, or any other event that emphasizes the spine beyond its breaking point [1]. In a simulation of the fracture in recent studies, loads are applied to real vertebral samples (destructive tests), where both compression loads are fixed on the upper and lower faces of the vertebral body. The literature mentions tests with loads of approximately 8000N emulating a daily accident, so this research aims to obtain a precise model with the use of an optical scanner, which will allow the obtaining of points (meshing) of the piece in real time with an individual measurement of up to 16 million independent measurement points captured from 1 to 2 seconds. The measurement data is characterized by a very detailed reproduction and therefore also allows the measurement of sample components up to 38mm.
The objective of this research was to reduce cost of maintenance in this equipment. Analysis on a blade of gas turbine was performed, which was in operation on an offshore platform. Compressor blade was exposed to a severe damage by the impact of particles and environmental pollutants such as salts, sands and sulphurs. In first stage of this analysis, a visual inspection with a borescope was effected. Images analysis was used to determine the typical failure modes. A tribological characterization was carried out. Chemical composition of the material of blades was obtained. Scanning electron microscopy (SEM) was used to measure chemical properties and evaluate degradation of surfaces of blades after 30,000 service hours. The points, where there are the points in which failures take place. Results showed wear modes were originated by a severe stinging action. Also, large craters, similar to those observed in solid particle erosion, were developed by at normal impact. In the same way it could be found some localized areas with corrosion and irregular scratches like plowing action, was observed; these are the points in which failures take place.
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