This present describes the results of the evaluation regarding the self-perception of personal and social attitudes acquired by university students from an engineer-ing faculty at a state university in Peru, in the context of virtual teaching and learning, declared by the health emergency by COVID-19; For which the follow-ing objectives were proposed, to determine the variation or impact that the self-perception of personal and social attitudes experienced, having as reference sce-narios, the academic semester with face-to-face teaching (academic semester 2019B) and then the academic semester developed totally virtual (2020A). An exploratory-descriptive research level was used, with a longitudinal non-experimental design, whose population and sample is made up of 674 and 761 students, in the 2019B and 2020A semesters respectively. The data collection in-struments were validated through Cronbach's Alpha, whose average results per academic semester were 0.960 and 0.958. After the investigation, it was found that there is no negative impact, due to virtual teaching; On the contrary, on aver-age, there was an increase in all levels of satisfaction, increasing the level very satisfied by 52.8% and the level satisfied by 3.25%.

This article aims to determine to what extent the self-perception of acquiring professional skills has been affect ed, by the context of the Covid-19 pandemic in University Students of Business Administration; For which, the results obtained in two satisfaction survey processes, carried out in academic semesters 2019-B and 2020-A, have been compared, marking a before and after in relation to the declaration of a state of emergency in Peru. Initially, it was determined that the indicators "To solve specialty cases" and "To assume self-education" are the most affected in this dimension, with a percentage of decrease in satisfaction of 3.42% and 3.82%, respectively. Then it was determined through the use of the statistical analysis of crossed tables, that the percentage of totally dissatisfied students, in the self-perception of having acquired the competences referred to the two indicators with the greatest negative impact, has remained invariant, almost constant, around of 44%. With this, it can be concluded that the self-perception of acquiring professional skills has been affected, by the context of the Covid-19 pandemic, decreasing by 3.1%, student satisfaction, as observed in the two indicators with the greatest impact negative "To assume self-education", and "To solve specialty cases". These results will allow the Public University of Peru to establish improvement plans, in order to advance towards the development of the teaching-learning model in a virtual way in higher education.

This article aims to describe the design of an automatic control system for the automated management of motor drives through Simocode pro; and determine the effect from the quantitative point of view on the performance of the filling and dispatch process of chemical inputs from the perspective of dispatch time and the amount of input spilled in the tank filling stage. For this, the programming of the programmable logic controller was carried out using the simatic step 7 software, then the distributed control system (DCS Siemens S410) was programmed, using the PCS7 V8.1 software, where the control logic and simocode pro integration is carried out through the profibus protocol, which is monitored from an human-machine interface (HMI) interface. Once the control system was implemented, it was possible to reduce the operating time from 60 minutes on average to 35 minutes, which reflects an improvement of 41.66%; this in turn generates an increase in the number of tank filling by 62.84%. Likewise, it is possible to reduce the amount of chemical inputs spilled in the filling stage; this improvement represents 88.60%.

No abstract

The objective of this study is to analyze and discuss the metrics of the Machine Learning model through the Ensemble Bagged Trees algorithm, which will be applied to data on satisfaction with teaching performance in the virtual environment. Initially the classification analysis through the Matlab R2021a software, identified an Accuracy of 81.3%, for the Ensemble Bagged Trees algorithm. When performing the validation of the collected data, and proceeding with the obtaining of the predictive model, for the 4 classes (satisfaction levels), total precision values of 82.21%, Sensitivity of 73.40%, Specificity of 91.02% and of 90.63% Accuracy. In turn, the highest level of the area under the curve (AUC) by means of the Receiver operating characteristic (ROC) is 0.93, thus considering a sensitivity of the predictive model of 93%. The validation of these results will allow the directors of the higher institution to have a database, to be used in the process of improving the quality of the educational service in relation to teaching performance.