Artificial lighting is a key factor in Closed Production Plant Systems (CPPS). A significant light-emitting diode (LED) technology attribute is the emission of different wavelengths, called light recipes. Light recipes are typically configured in continuous mode, but can also be configured in pulsed mode to save energy. We propose two nonlinear models, i.e., genetic programing (GP) and feedforward artificial neural networks (FNNs) to predict energy consumption in CPPS. The generated models use the following input variables: intensity, red light component, blue light component, green light component, and white light component; and the following operation modes: continuous and pulsed light including pulsed frequency, and duty cycle as well energy consumption as output. A Spearman’s correlation was applied to generate a model with only representative inputs. Two datasets were applied. The first (Test 1), with 5700 samples with similar input ranges, was used to train and evaluate, while the second (Test 2), included 160 total datapoints in different input ranges. The metrics that allowed a quantitative evaluation of the model’s performance were MAPE, MSE, MAE, and SEE. Our implemented models achieved an accuracy of 96.1% for the GP model and 98.99% for the FNNs model. The models used in this proposal can be applied or programmed as part of the monitoring system for CPPS which prioritize energy efficiency. The nonlinear models provide a further analysis for energy savings due to the light recipe and operation light mode, i.e., pulsed and continuous on artificial LED lighting systems.

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.

The integration of LED lighting enables precise radiation control in plant factory cultivation systems. While LEDs offer energy efficiency and spectral tuning, achieving a uniform photosynthetic photon flux density (PPFD) remains a critical technical challenge. This study evaluated the impact of three spatial LED configurations on irradiance uniformity using commercial horticultural LEDs and a light recipe of 75% red and 25% blue. Optical simulations in TracePro^® 2017 were conducted to analyze radiant flux, optical efficiency, and uniformity, along with LED quantity, system cost, and electrical consumption under two environmental scenarios: open (without reflective walls) and closed (with reflective walls). Results show that distribution 3, which featured reduced central LED density, achieved 4–8% higher homogeneity in the open scenario, and 2.7–6.5% in the closed scenario, compared to symmetric layouts (distribution 1 and 2). Reflective walls increased average PPFD by up to 20% and optical efficiency by around 9%, with a minimal effect on uniformity. Lowering the lamp-to-canopy distance from 35 cm to 30 cm resulted in a 10% increase in PPFD. Despite a reduction in total photon flux, distribution 3 exhibited superior irradiance homogeneity. One-way ANOVA confirmed significant effects of environment, height, and LED model (p < 0.05), but not of spatial alone. This simulation-based methodology offers a robust framework for optimizing energy-efficient lighting systems. Future work will explore the integrating of non-visible wavelengths and experimental validations to extend practical applicability.