The quality assessment and grading of agricultural products is one of the post-harvest activities that has received considerable attention due to the growing demand for healthy and better-quality products. Recently, various non-destructive methods have been used to evaluate the quality of agricultural products, which are very desirable and faster and more economical than destructive methods. Optical methods are one of the most important non-destructive methods that use the high speed of light detection and computer data processing and are able to evaluate the quality and classification of products with high accuracy. Among the optical methods, visible–near-infrared (Vis/NIR) spectroscopy is considered one of the most accurate methods. In this research, Vis/NIR spectroscopy technology was used in the spectral range of 350–1150 nm for non-destructive detection of some quality parameters including pH, TA, SSC, and TP of two varieties of Red Delicious and Golden Delicious apples. Various pre-processing models were developed to predict the parameters, which brought the desired results with high accuracy so that pH prediction results were for yellow apples (RMSEC = 0.009, rc = 0.991, SDR = 2.51) and for red apples (RMSEC = 0.005, rc = 0.998, SDR = 2.56). The results for TA were also (RMSEC = 0.003, rc = 0.996, SDR = 2.51) for red apples and (RMSEC = 0.001, rc = 0.998, SDR = 2.81) for yellow apples. The results regarding SSC were for red apples (RMSEC = 0.209, rc = 0.990 and SDR = 2.82) and for yellow apples (RMSEC = 0.054, SDR = 2.67 and rc = 0.999). In addition, regarding TP, the results were for red apples (RMSEC = 0.2, rc = 0.989, SDR = 2.05) and for yellow apples (RMSEC = 1.457, rc = 0.998, SDR = 1.61). The obtained results indicate the detection of the mentioned parameters with high accuracy by visible/infrared spectroscopic technology.
Actualmente, la población mexicana desconoce la probabilidad de presentar eventos agravantes (intubación, ingreso a la unidad de cuidados intensivos y defunción) derivados del COVID-19. Diversos autores han propuesto modelos gráficos probabilísticos para identificar los factores asociados a esta enfermedad. En este documento, se propone utilizar redes bayesianas para identificar las relaciones de dependencia probabilística en 23 variables de estudio del conjunto de datos abiertos de COVID-19, proporcionado por la Dirección General de Epidemiología en México durante el periodo 2020 y 2021. Se generaron modelos de redes bayesianas a través de los algoritmos de aprendizaje estructural: PC y Hill Climb Search. Los resultados permitieron determinar que la diabetes, hipertensión y obesidad son los principales factores que inciden en eventos agravantes de COVID-19, así mismo, la probabilidad de defunción depende del grupo de edad del paciente y si fue o no intubado. La red bayesiana como clasificador obtiene al menos un 94% de precisión y exactitud al clasificar eventos agravantes de COVID-19.
En este artículo, se plasma el trabajo interdisciplinario de la Maestría en Sistemas Computacionales con apoyo del CONACYT, impartida en el Tecnológico Nacional de México/Instituto Tecnológico de Acapulco, para presentar la propuesta para el desarrollo de un sistema de bitácoras para facilitar el registro y manejo de los clientes del departamento de Telemarketing del complejo Hotelero de Grupo Vidanta Acapulco. Con el desarrollo del sistema se pretende agilizar tiempos de operadores y personal a cargo de organizar las bitácoras de trabajo y captura de información, y así también tener centralizada la información confidencial de los clientes.
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