Los enfoques de prevención de riesgos en actividades, funciones o procesos se han convertido en piezas fundamentales a la hora de minimizar la ocurrencia de eventos que son perjudiciales para las compañías. Cada producto no conforme está estrechamente ligado con eventos no deseados relacionados con uno o algunos de los factores que intervienen en el proceso. La identificación, análisis, evaluación, tratamiento, comunicación y monitoreo de estos eventos no deseados garantizarán el incremento de la calidad en los productos y la productividad en el proceso productivo. En este artículo, proponemos un diseño metodológico para la prevención de riesgos en procesos productivos. La metodología propone una forma novedosa de combinar el uso de herramientas estadísticas de calidad y la norma ISO 31000 de gestión de riesgos. La validación fue hecha sobre un proceso de envasado de productos lácteos. Las conclusiones de esta investigación muestran que el diseño metodológico propuesto es suficientemente flexible para ser adaptado a cualquier tipo de proceso de fabricación que se desea monitorear y mejorar. Palabras clave: Control de calidad, proceso de gestión de riesgos, mejora de calidad, herramientas de estadísticas.

This paper proposes the analysis of the influence of terms that express feelings in the automatic detection of topics in social networks. This proposal uses an ontology-based methodology which incorporates the ability to identify and eliminate those terms that present a sentimental orientation in social network texts, which can negatively influence the detection of topics. To this end, two resources were used to analyze feelings in order to detect these terms. The proposed system was evaluated with real data sets from the Twitter and Facebook social networks in English and Spanish respectively, demonstrating in both cases the influence of sentimentally oriented terms in the detection of topics in social network texts.

The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually and at his own discretion, which has a number of associated drawbacks. In this work, a solution is proposed that includes the planting, design, development and testing of a prototype that is able to correctly classify photographs corresponding to samples of raw material arrived at a dryer, using intelligence techniques (IA) type supervised for Classification by Artificial Neural Networks and not supervised with K-means Grouping for class preparation. The prototype performed well and is a reliable tool for classifying the raw material slammed into tea dryers.

Published 15 September 2020 This article, and others within this volume, has been retracted by IOP Publishing following clear evidence of plagiarism and citation manipulation. This work was originally published in Spanish (1) and has been translated and published without permission or acknowledgement to the original authors. IOP Publishing Limited has discovered other papers within this volume that have been subjected to the same treatment. This is scientific misconduct. Misconduct investigations are ongoing at the author’s institutions. IOP Publishing Limited will update this notice if required once those investigations have concluded. To date, there is no evidence to suggest anyone other than Amelec Viloria / Jesus Silva was directly culpable for the actions that led to retraction. IOP Publishing Limited request any citations to this article be redirected to the original work (1). Anyone with any information regarding these papers is requested to contact conferenceseries@ioppublishing.org.

Agriculture plays an important role in Latin American countries where the demand for provisions to reduce hunger and poverty represents a significant priority in order to improve the development and quality of life in the region. In this research, linear data analysis techniques and soil classification are reviewed through neural networks for decision making in agriculture. The results permit to conclude that precision agriculture, observation and control technologies are gaining ground, making it possible to determine the production demand in these countries.