Coronavirus disease 2019 (COVID-19) has been the most recent disease, with millions of deaths worldwide. Fortunately, vaccines have been developed to immunize the population. However, the distribution of the vaccine is also a significant challenge. Generally, each country defines its strategy to bring it closer to the target population, considering the adequate transport for their conservation, depending on the type of vaccines defined. In this work, a mathematical model is proposed for the distribution of different COVID-19 vaccines in Mexico. Our results suggest an efficient strategy to meet demand in a given period.

Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.

Purpose: The purpose of this paper is to propose a novel model, to forecast demand for a third-party service by using the Grey Systems Theory (GST) and Markov Chains, where its forecast error performance is evaluated through mean percentage error, mean absolute percentage error, where it exceed other models' performance accuracy such as autoregressive integrated moving average. Design/methodology/approach: The model performs data characterization to qualify the GM (1,1) model and then applies a Markov Chain transition probability matrix and the GM (1,1) to forecast a time series with high degree of vagueness and imprecision and by providing a forecast kernel range ⊗Aˆ. Findings: The MCGM (1,1) model integrates the GST GM (1,1) and Markov Chains in a novel hybrid model, that reduces the mathematical calculation complexity while provides practical forecast performance that exceed or it is equally good as other traditional methods. Research limitations/implications: The model outperforms other non-stationary models but does not incorporate multiple variables and requires additional mathematical treatment or combined methods, where its data is stationary, seasonal or negative. Practical implications: This model can provide an accurate forecast projection of supply chain demand, for instance the space required in a third-party logistics services provider in Tijuana Mexico, it can be used to forecast complex supply chain systems with minimum, incomplete or poor data, to solve several practical application problems to forecast demand and resources. Social implications: The novel MCGM (1,1) hybrid forecasting model combines multiple predictive approaches, allowing for greater accuracy and adaptability. Its implementation enhances decision-making in key sectors such as health, energy and manufacturing, optimizing resources and reducing costs. This drives economic growth, increases sustainability and improves the quality of life in society. Originality/value: There are no MCGM (1,1) works applied in supply chain and current works have not established the model characterization criteria. The result of this investigation represents a novel proposal to solve uncertain models with poor information and small amounts of data (>4 records), with higher forecast accuracy.