Confinement as a result of COVID-19 had a strong impact around the world and restricted mobility. The university community started to take routine classes in a virtual and sedentary way, causing negative effects on their health and habits. The objective of this research is to analyze the impact of confinement through surveys of students and interviews with university professors, in order to study the effects of confinement on physical activity, emotional state, and health. The methodology was as follows: (i) preliminary data; (ii) survey development, interviews, and information collection; (iii) data processing and multivariate presentation of the results, using multiple correspondence analysis (MCA) and multiple factor analysis (MFA). The results of 375 respondents show that there is a low level of physical activity (<300 METs, 49.6%), where women register sedentary behavior (73%). Emotionally, most of them show feeling bored. Some express anxiety, depression, discomfort, and frustration. In terms of health, there are rheumatic, circulatory, respiratory, and other diseases related to obesity. It is essential to create programs that promote physical exercise to reduce the consequences of sedentary lifestyles on the physical, social, and mental health of university students, especially engineering students, who experienced greater effects of confinement than those studying nutrition and social sciences.

The analysis of variance is a statistical technique widely used in the design of experiments and different research areas. It has been modeled using a classical or frequentist approach. With the computational power that is currently available, the Bayesian approach is an essential statistical tool related to hypothesis testing. However, conformity with classical techniques, ignorance of Bayesian statistics, and lack of easy-to-use software are obstacles to its frequent application. In this work, the use of a reproducible statistical package in R is proposed. It presents options to perform an analysis of variance in a classical (frequentist) and Bayesian way when the assumptions of the frequentist approach are not met or when a level of more specific inference such as quantifying the evidence provided by a data set for a given hypothesis, with the possibility of contributing to the understanding of the rejection or not of the statistical hypotheses raised, through interactive graphics presented in an emerging Shiny panel.

Models implemented in statistical software for the precision analysis of diagnostic tests include random-effects modeling (bivariate model) and hierarchical regression (hierarchical summary receiver operating characteristic). However, these models do not provide an overall mean, but calculate the mean of a central study when the random effect is equal to zero; hence, it is difficult to calculate the covariance between sensitivity and specificity when the number of studies in the meta-analysis is small. Furthermore, the estimation of the correlation between specificity and sensitivity is affected by the number of studies included in the meta-analysis, or the variability among the analyzed studies. To model the relationship of diagnostic test results, a binary covariance matrix is assumed. Here we used copulas as an alternative to capture the dependence between sensitivity and specificity. The posterior values were estimated using methods that consider sampling algorithms from a probability distribution (Markov chain Monte Carlo), and estimates were compared with the results of the bivariate model, which assumes statistical independence in the test results. To illustrate the applicability of the models and their respective comparisons, data from 14 published studies reporting estimates of the accuracy of the Alcohol Use Disorder Identification Test were used. Using simulations, we investigated the performance of four copula models that incorporate scenarios designed to replicate realistic situations for meta-analyses of diagnostic accuracy of the tests. The models’ performances were evaluated based on p-values using the Cramér–von Mises goodness-of-fit test. Our results indicated that copula models are valid when the assumptions of the bivariate model are not fulfilled.

Processes within the supermarket retail sector necessitate continuous enhancements to minimize expenditures and maintain competitiveness. This research endeavors to develop a model and simulate the gondola replenishment process at a point of sale located in Guayaquil, Ecuador. The modeling incorporates relevant factors such as merchandise restocking to estimate the personnel required for adequate product replenishment. The simulation methodology allows for modeling a point of sale through various merchandise supply scenarios and estimating the optimal number of personnel needed. The simulation and its components are established based on observed processes, with merchandise boxes identified as the primary unit of measure within the model, and data asymmetry evaluated using probability distributions. This modeling is executed using queuing theory and evolutionary algorithms via Promodel software, with the SimRunner optimizer used to identify the optimal staffing levels. Given the complexities associated with mathematical programming and specialized software such as the General Algebraic Modeling System, this research presents an accessible and readily applicable alternative solution for planning managers.

The discriminatory capacity of a test is commonly expressed in terms of sensitivity and specificity, and there is generally a compromise relationship between these two measures, as an increasing threshold for defining the positivity of the test results in a decrease in sensitivity and an increase in specificity. Recommended methods for the meta-analysis of diagnostic tests, such as the bivariate model, focus on estimating a summary sensitivity and specificity at a common threshold, while the Hierarchical Summary Receiver Operating Characteristic (HSROC) model focuses on estimating a summary curve from studies that have used different thresholds. Therefore, we will explain the hierarchical modeling for meta-analysis of the study of precision in diagnostic tests, and we will design a decision scheme that helps to understand the models to choose the most appropriate in situations of heterogeneity in the studies, and illustrate its application in a systematic review, studying the properties and assumptions of meta-analytic procedures to synthesize the quantitative evidence of the parameters. For which, we used a systematic review that obtained summary estimates for the diagnosis of invasive aspergillosis, being our modeling framework the NLMIXED procedure of SAS, obtaining summary estimates for sensitivity and specificity for the bivariate model of 0.7708 and 0.8521, from a total of 27 studies involving 3,943 patients, and for the HSROC case the values were 0.7304 and 0.8867 respectively. Finally, we hope that this article will provide clinicians with a sufficient understanding of the terminology and statistical methods, obtaining plausible interpretations of the results in systematic reviews.