Continuous and Pulsed Ultraviolet-C LED on Germicidal Effect and Energy Consumption in Fresh Products: Applications in Tomatoes as a Model.

Nowadays, lifestyle change is one of the problems of the new world economic order, and the procedures of feeding, purchasing, preparation, and the storage of food products, are forcing authorities to establish more rigorous methods concerning the control of food quality and safety. Owing quality in the agro-food sector is a complex and global issue, due to the distance between production and final consumption, as well as the new demands of society on food. Contributing to the bacteria minimization during their path in the supply chain, the objective of this research is the use of an UV-C LED artificial lighting system with emission in continuous light (CL) and two of pulsed light (Mode 1 and Mode 2) for fresh products' disinfection. A mathematical model is introduced as a reference to establish the equivalence dose of continuous and pulsed UV-C LED irradiation. The doses applied were 5, 15, and 25 mJ cm-². The configured parameters per each technique were the irradiance, time also the frequency (500 Hz), and duty cycle (30, 50, and 80%) for Mode 1 and Mode 2. The germicidal effect (GE), energy consumption, and effective germicidal effect (EGE), were evaluated for the different techniques. According to the results, the technique Mode 1 was the best in the GE with 1.06 ± 0.01 and 1.08 ± 0.01 Log reduction by 25 mJ cm-2 at 30 and 80% duty cycle, correspondingly. The CL and Mode 1 showed an outstanding performance with the EGE. Finally, Mode 1 reduced 11% in energy and the GE is comparable with CL. The pulsed light technique Mode 1 constitutes a powerful method against the microorganism's destruction and a strategy for saving energy during the treatment. The UV-C LEDs proved to be an excellent alternative in the disinfection of fresh products with pulsed light emission in the real process.

Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques.

The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico.