This study proposes a new index to measure the resilience of an individual to stress, based on the changes of specific physiological variables. These variables include electromyography, which is the muscle response, blood volume pulse, breathing rate, peripheral temperature, and skin conductance. We measured the data with a biofeedback device from 71 individuals subjected to a 10-min psychophysiological stress test. The data exploration revealed that features’ variability among test phases could be observed in a two-dimensional space with Principal Components Analysis (PCA). In this work, we demonstrate that the values of each feature within a phase are well organized in clusters. The new index we propose, Resilience to Stress Index (RSI), is based on this observation. To compute the index, we used non-supervised machine learning methods to calculate the inter-cluster distances, specifically using the following four methods: Euclidean Distance of PCA, Mahalanobis Distance, Cluster Validity Index Distance, and Euclidean Distance of Kernel PCA. While there was no statistically significant difference (p>0.01) among the methods, we recommend using Mahalanobis, since this method provides higher monotonic association with the Resilience in Mexicans (RESI-M) scale. Results are encouraging since we demonstrated that the computation of a reliable RSI is possible. To validate the new index, we undertook two tasks: a comparison of the RSI against the RESI-M, and a Spearman correlation between phases one and five to determine if the behavior is resilient or not. The computation of the RSI of an individual has a broader scope in mind, and it is to understand and to support mental health. The benefits of having a metric that measures resilience to stress are multiple; for instance, to the extent that individuals can track their resilience to stress, they can improve their everyday life.

To understand and approach the spread of the SARS-CoV-2 epidemic, machine learning offers fundamental tools. This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. The methods compared are linear, polynomial, and generalized logistic regression models to describe the growth of COVID-19 incidents in Mexico. Additionally, machine learning and time series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with the mobility rates obtained from Google’s Mobility Reports and climate variables acquired from the Weather Online API. The results suggest that the logistic growth model fits best the pandemic’s behavior, that there is enough correlation of climate and mobility variables with the disease numbers, and that the Long short-term memory network can be exploited for predicting daily cases. Given this, we propose a model to predict daily cases and fatalities for SARS-CoV-2 using time series data, mobility, and weather variables.

Background This paper explores machine learning algorithms and approaches for predicting alum income to obtain insights on the strongest predictors and a ‘high’ earners’ class. Methods It examines the alum sample data obtained from a survey from a multicampus Mexican private university. Survey results include 17,898 and 12,275 observations before and after cleaning and pre-processing, respectively. The dataset comprises income values and a large set of independent demographical attributes of former students. We conduct an in-depth analysis to determine whether the accuracy of traditional algorithms can be improved with a data science approach. Furthermore, we present insights on patterns obtained using explainable artificial intelligence techniques. Results Results show that the machine learning models outperformed the parametric models of linear and logistic regression, in predicting alum’s current income with statistically significant results (p < 0.05) in three different tasks. Moreover, the later methods were found to be the most accurate in predicting the alum’s first income after graduation. Conclusion We identified that age, gender, working hours per week, first income and variables related to the alum’s job position and firm contributed to explaining their current income. Findings indicated a gender wage gap, suggesting that further work is needed to enable equality.

Background: To understand and approach the COVID-19 spread, Machine Learning offers fundamental tools. This study presents the use of machine learning techniques for the projection of COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. Methods: The methods used are linear, polynomial, and generalized logistic regression models to evaluate the growth of the COVID-19 incidents in the country. Additionally, machine learning and time-series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with mobility rates obtained from Google’s Mobility Reports and climate variables acquired from Weather Online. Results: The results suggest that the logistic growth model fits best the behavior of the pandemic in Mexico, that there is a significant correlation of climate and mobility variables with the disease numbers, and that LSTM is a more suitable approach for the prediction of daily cases. Conclusion: We hope that this study can make some contributions to the world’s response to this epidemic as well as give some references for future research.

Background: This paper explores different machine learning algorithms and approaches for predicting alum income to obtain insights on the strongest predictors for income and a ‘high’ earners’ class. Methods: The study examines the alum sample data obtained from a survey from Tecnologico de Monterrey, a multicampus Mexican private university, and analyses it within the cross-industry standard process for data mining. Survey results include 17,898 and 12,275 observations before and after cleaning and pre-processing, respectively. The dataset includes values for income and a large set of independent variables, including demographic and occupational attributes of the former students and academic attributes from the institution’s history. We conduct an in-depth analysis to determine whether the accuracy of traditional algorithms in econometric research to predict income can be improved with a data science approach. Furthermore, we present insights on patterns obtained using explainable artificial intelligence techniques. Results: Results show that the gradient boosting model outperformed the parametric models, linear and logistic regression, in predicting alum’s current income with statistically significant results (p < 0.05) in three tasks: ordinary least-squares regression, multi-class classification and binary classification. Moreover, the linear and logistic regression models were found to be the most accurate methods for predicting the alum’s first income. The non-parametric models showed no significant improvements. Conclusion: We identified that age, gender, working hours per week, first income after graduation and variables related to the alum’s job position and firm contributed to explaining their income. Findings indicated a gender wage gap, suggesting that further work is needed to enable equality.