Wilfrido Jacobo Paredes García

The contingency measures put in place by the government during COVID-19 exposed the students to a new condition to which they must adapt. To understand how the students perceive and cope during the unplanned, changed learning mode, we conducted a study using an evaluation tool which seeks to understand the effect of the contingency measures associated with the emergence of the COVID-19 virus on the students. By assigning a data collection instrument to students who are part of 15 technological universities (TUs) and 7 polytechnic universities (PUs), we determined how they were affected by COVID-19. The questions intended to evaluate the social, economic, academic, emotional, and health effects experienced. A total of 6596 students were assessed in the study representing an appropriate percentage of the Mexican students. The outcome of the study showed that 12% of the students agreed with the online approach to learn adopted because of the contingency. A total of 39% reported that they have a good environmental space for online learning, 32% reported that they mostly take their classes via their mobile phones, and 3% said they lacked access to an internet facility and as such could not take their classes. A total of 14% reported that they have little access to the internet, while 42% reported that they regularly have internet, and both complained that internet fluctuation significantly affects their academic performance. Comparing the different modes of teaching, 52% believe an in-person class is the best approach to learning, but 22% agreed that a hybrid system will be effective. Through a multiple correspondence analysis (MCA) it was determined that, in the effects, there was no significant difference in relation to gender. The effects that most impacted the students were economic, connectivity, and a lack of physical activity.

University teachers have adapted to different situations during the development of distance learning due to the pandemic caused by the COVID-19 virus. This study was conducted by assigning a data collection instrument to 993 teachers who are part of 15 technological universities (TUs) and 7 polytechnic universities (PUs) to determine how they were affected by COVID-19. The questions asked were related to the social, economic, academic, emotional, and health effects experienced. The results show that 63% of the teachers working online complained that online teaching invaded their family privacy; 56% pointed out that working from home and the virtual classes affected their performance as teachers; 90% of the teachers thought that they dedicated too much extra to preparing for their classes; 15% were stressed; 4% felt negative under the new teaching scheme of virtual classes; finally, 38% of the teachers stated that repeated interaction with electronic devices had a lot of negative impacts on their emotional wellbeing. By means of a G-test, it was determined that gender was independent from the studied effects. Through a multiple correspondence analysis (MCA), it was determined that, of the total number of teachers who responded to the questionnaire, half were comfortable with the online teaching model and the other half were not. The most impacted effects were the economic, training and connectivity independently from the gender.

Dementia is a neurodegenerative disease that leads to the development of cognitive deficits, such as aphasia, apraxia, and agnosia. It is currently considered one of the most significant major medical problems worldwide, primarily affecting the elderly. This condition gradually impairs the patient’s cognition, eventually leading to the inability to perform everyday tasks without assistance. Since dementia is an incurable disease, early detection plays an important role in delaying its progression. Because of this, tools and methods have been developed to help accurately diagnose patients in their early stages. State-of-the-art methods have shown that the use of syntactic-type linguistic features provides a sensitive and noninvasive tool for detecting dementia in its early stages. However, these methods lack relevant semantic information. In this work, we propose a novel methodology, based on the semantic features approach, by using sentence embeddings computed by Siamese BERT networks (SBERT), along with support vector machine (SVM), K-nearest neighbors (KNN), random forest, and an artificial neural network (ANN) as classifiers. Our methodology extracted 17 features that provide demographic, lexical, syntactic, and semantic information from 550 oral production samples of elderly controls and people with Alzheimer’s disease, provided by the DementiaBank Pitt Corpus database. To quantify the relevance of the extracted features for the dementia classification task, we calculated the mutual information score, which demonstrates a dependence between our features and the MMSE score. The experimental classification performance metrics, such as the accuracy, precision, recall, and F1 score (77, 80, 80, and 80%, respectively), validate that our methodology performs better than syntax-based methods and the BERT approach when only the linguistic features are used.

In this project, an analysis is made of the perception of teachers of Higher Education Institutions (HEI) regarding the use of Virtual Learning Platforms (VLP) in the transition from the Traditional Educational Model (face-to-face) to Emergency Remote Teaching (ERT). A statistical analysis of teachers’ views on the academic environment caused by the COVID-19 crisis is carried out for the change of educational scenarios from school to home through VLP, the support for teaching–learning knowledge of VLPs and the received training, and the main problems during the transition period. Through convenience sampling, data was collected for a statistical study using a developed instrument (Data collection was designed through the Google Forms application and distributed to public and private HEI teachers). The results of the study showed that more than 60% of respondents had experience using Moodle, Google Classroom, and Blackboard; 80% of teachers had training from their institution for the use of virtual platforms; and in 60% of cases, higher education institutions allowed them to choose the VLP. In addition, the main issues they faced were connectivity, student attitude, and student attendance at class sessions. Fisher’s test was conducted to determine the relationship in the variables analyzed by identifying that there are differences of teachers in perception depending on age.

This descriptive study intends to identify the satisfaction perception among the teachers of the Universidad del Valle de México (UVM) concerning the use of the Microsoft Teams platform in the transition from traditional model (face-to-face) to 100% online education [Emergency Remote Teaching (ERT)]. The proposal aims to determine the perspectives of teachers regarding the use of the Microsoft Teams platform during the crisis caused by COVID-19. UVM has 6938 full-time teachers and part-time teachers who collaborated in educational programs during January-June 2020 in the 33 campuses of UVM. And an instrument was developed and applied using finite population sampling, UVM perspective of teachers, which was distributed via Google Forms. The feasibility of the data collection instrument was determined by the Cronbach’s Alpha coefficient, with a result of 0.926. The data collection period was aligned with the first isolation period: 23 March to 20 April. The results in the perception of teacher satisfaction in the different sections of the instrument established an agreement in the answers (very satisfied or satisfied) regarding values that were higher than 60% in terms of satisfaction using the equipment. The analysis of the data collected was performed to verify the proposed hypothesis with the R version 4.0 software. A G-test was performed with the Logverosimilitude coefficient to test whether the categorical variables were independent (qualitative variables that are not defined continuously). The Krammer coefficient of association was then calculated to measure the correlation.

At the onset of the crisis caused by COVID-19, the Mexican education system chose to join the global context and suspend face-to-face classes for all educational levels. For the continuity of educational processes, a transition from a traditional educational model (face-to-face) to emergency remote teaching (ERT) was made through virtual learning platforms and learning management system (LMS) schemes. Universidad del Valle de Mexico (UVM), in a collaboration agreement with Microsoft Co., chose to use Teams to continue its educational process. In this work, we intend to identify the factors that can be taken into account regarding the level of student satisfaction in the teaching–learning process in ERT using Teams, and validate the established educational strategy. Statistical analysis was carried out to analyze the academic environment for these scenario changes while considering knowledge assessment, and competencies achievement. A combined sampling method was applied with convenience and statistical analysis. The main results established significant percentages, where more than 60% of the students surveyed were manifested in the use of the teams and the organization of the class sessions by the teachers, and the activities developed. Using the Cronbach’s Alpha coefficient, the reliability of the data collection instruments was determined. The correlations of each of the survey questions were calculated to determine the relationship between themselves and the total answers, giving results similar to those obtained through data science tools. Taking advantage of the situation, data science tools were applied to compare the results with obtained values from RapidMiner software in the correlation of factors in of 0.440, 0.384, 0.246, 0.048 and 0.384.

Decision-making based on data analysis leads to knowing market trends and anticipating risks and opportunities. These allow farmers to improve their production plan as well as their chances to get an economic success. The aim of this work was to develop a methodology for price forecasting of fruits and vegetables using Queretaro state, MX as a case study. The daily prices of several fruits and vegetables were extracted, from January 2009 to February 2019, from the National System of Market Information. Then, these prices were used to compute the weekly average price of each product and their span commercialization in Q4 and over the median of historical data. Moreover, product characterization was performed to propose a methodology for future price forecasting of multiple agricultural products within the same mathematical model and it resulted in the identification of 18 products that fit the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model. Finally, future price estimation and validation was performed to explain the product price fluctuations between weeks and it was found that the relative error for most of products modeled was less than 10%, e.g., Hass avocado (7.01%) and Saladette tomato (8.09%). The results suggest the feasibility for the implementation of systems to provide information for better decisions by Mexican farmers.

The main focus of this research was to develop an approach using statistical tools and Data envelopment analysis (DEA) to tackling productivity measurements and benchmarking problems in electrical conductor manufacturing environment. In the present work, a tooling efficiency study was carried out with a nozzle used for the manufacture of 23-AWG wires. The efficiency of five types of tooling, four non-Mexican-manufactured types and one Mexican-manufactured type, were compared. Analysis of Variance (ANOVA) and the Tukey test were applied. Six factors were considered that influence of the performance of the tooling during the manufacturing process: productivity, quality, time, machine, operator, and color of the insulating material, but the research work focuses on the efficiency of the tooling die-nozzle. The results demonstrated that two die-nozzle models exhibited the best performance; one of them was the Mexican model, surpassed by a non-Mexican model, the capability process index Cpk = 1.26 manifested a better performance for the 3DND die-nozzle according to the statistical analysis and the tests performed. Subsequently, through a super-efficiency DEA model of inputs-oriented with non-decreasing returns to scale (NDRS). The results obtained in the statistical analysis were corroborated using this technique, its application combined with statistical tools represents an innovation for knowledge in manufacturing processes of electrical conductors. Input data were obtained at a manufacturer of electrical conductors supplier of the automotive sector in the Querétaro City of Mexico.

The number and position of sEMG electrodes have been studied extensively due to the need to improve the accuracy of the classification they carry out of the intention of movement. Nevertheless, increasing the number of channels used for this classification often increases their processing time as well. This research work contributes with a comparison of the classification accuracy based on the different number of sEMG signal channels (one to four) placed in the right lower limb of healthy subjects. The analysis is performed using Mean Absolute Values, Zero Crossings, Waveform Length, and Slope Sign Changes; these characteristics comprise the feature vector. The algorithm used for the classification is the Support Vector Machine after applying a Principal Component Analysis to the features. The results show that it is possible to reach more than 90% of classification accuracy by using 4 or 3 channels. Moreover, the difference obtained with 500 and 1000 samples, with 2, 3 and 4 channels, is not higher than 5%, which means that increasing the number of channels does not guarantee 100% precision in the classification.