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.

Although measures such as sensitivity and specificity are used in the study of diagnostic test accuracy, these are not appropriate for integrating heterogeneous studies. Therefore, it is essential to assess in detail all related aspects prior to integrating a set of studies so that the correct model can then be selected. This work describes the scheme employed for making decisions regarding the use of the R, STATA and SAS statistical programs. We used the R Program Meta-Analysis of Diagnostic Accuracy package for determining the correlation between sensitivity and specificity. This package considers fixed, random and mixed effects models and provides excellent summaries and assesses heterogeneity. For selecting various cutoff points in the meta-analysis, we used the STATA module for meta-analytical integration of diagnostic test accuracy studies, which produces bivariate outputs for heterogeneity.

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.

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.

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.

Background: Multivariate analysis is fast becoming a key instrument that can be used to address crimes or incidents. It may be helpful to assess government policies on crime prevention. Methods: To distinguish between the 25 official crime categories reported in Mexico City (Jan 2019 - Jun 2019), principal coordinates analysis was used to determine the quality of a characteristic in this context. This study used cluster analysis via K-means and Biplot based on time and location in terms of crime occurrence. Results: The results obtained from this preliminary analysis indicates that around 70% of crime occurrence is shown in the following boroughs: Cuauhtémoc, Iztapalapa, Gustavo A. Madero, Benito Juárez, Álvaro Obregón, Coyoacán, and Miguel Hidalgo. Conclusion: There are two factors that contribute to the difficulties in crime analysis in Mexico City, namely, the lack of people’s trust in authorities and the insufficiency of tools for data analysis. The latter is an integral part in achieving justice for the victims of crimes because it impedes the process of observing patterns and predicting the perpetrators’ next actions, which may help in solving a number of types of crimes. It is then imperative for law enforcement to utilize data analysis tools that aid in identifying crime patterns and trends, such that the occurrences of crime show a downward trend and consequentially increase the people’s trust in law enforcement agencies.