Student writing in the engineering curriculum: discursive rhetorical model of the laboratory report genre in Spanish.

Introduction: The laboratory report is a widely used genre in the academic training process in civil computer engineering. Students produce this genre in the university classroom for diverse academic and professional purposes. Despite its relevance, empirical rhetorical-discursive descriptions of the value of student writing are still scarce in Spanish. Thus, we describe the rhetorical-discursive organization of the laboratory report genre in this subdiscipline. Methods: To fulfil this purpose, we followed a methodological design based on Swalesian Genre Analysis and used a corpus of ninety-eight texts. The sample was collected in a self-compiled form through consultation with teachers and students in the university classroom. The application of this method allowed us to determine the macro-moves, moves, and rhetorical steps of the genre, its communicative functions, and textual features. Results: The resulting rhetorical model consisted of four macro-moves, twelve moves and seventeen steps. This model shows the highly dynamic and mesogeneric nature of this genre, the new functions of multimodal artefacts, and the genre's presence across the curriculum. To know about the teachers' and students' views on the process of training professional writers in engineering, the rhetorical model was complemented with an ethnographic phase (in the terms proposed by Swales: corroboration process with a couple of members of the community) before and after collecting the textual corpus. Discussion: Finally, the implications for genre theory, Spanish language theory and pedagogy of the Spanish language and genre pedagogy are discussed.

Comparative Analysis of Teaching at Public Universities in Sinaloa during Confinement Due to COVID-19.

The COVID-19 pandemic has affected educational institutions around the world. One partial solution for students and teachers to continue the academic process involved the use of software and hardware technologies via the internet. The main objective of this research was to analyze the actions carried out by computer science teachers (and teachers who taught related degrees) in Sinaloa, Mexico, during the COVID-19 confinement period, to determine if the working conditions were different at all educational institutions. Based on quantitative, descriptive-explanatory, correlational, field, and cross-sectional approaches to data collection-a survey was designed and sent to teachers who taught classes in computer science and related careers. The results showed that although teachers felt prepared in designing and implementing virtual courses (90.73%), 68.5% believed that virtual classes were not enough for students (i.e., regarding replacing the training being offered). Likewise, teachers observed that only 27.8% of their students showed real commitment to online classes. In the hypothesis test, a chi-squared value of 3.84 was obtained, with a significance (p-value) of 0.137. There was a probability of error of 13.7%; this is high, considering that the level of significance must be 0.05 (5%) or less. It was concluded that teachers must be permanently trained in the use of new digital technologies; in addition, they must continuously produce academic material and make it available to the educational community. It is necessary for universities to design plans for the regulated use of applications and devices for academic purposes, update study plans and programs, and train teachers and students beyond conventional education.

PXR is a target of (-)-epicatechin in skeletal muscle.

(-)-Epicatechin (EC) is a flavanol that has shown numerous biological effects such as: decrease risk of cardiovascular dysfunction, metabolism regulation, skeletal muscle (SkM) performance improvement and SkM cells differentiation induction, among others. The described EC acceptor/receptor molecules do not explain the EC's effect on SkM. We hypothesize that the pregnane X receptor (PXR) can fulfill those characteristics, based on structural similitude between EC and steroidal backbone and that PXR activation leads to similar effects as those induced by EC. In order to demonstrate our hypothesis, we: 1) analyzed the possible EC and mouse PXR interaction through in silico strategies, 2) developed an EC's affinity column to isolate PXR, 3) evaluated, in mouse myoblast (C2C12 cells) the inhibition of EC-induced PXR's nucleus translocation by ketoconazole, a specific blocker of PXR and 4) analyzed the effect of EC as an activator of mouse PXR, evaluating the expression modulation of cytochrome 3a11 (Cyp3a11) gen and myogenin protein. (-)-Epicatechin interacts and activates PXR, promoting this protein translocation to the nucleus, increasing the expression of Cyp3a11, and promoting C2C12 cell differentiation through increasing myogenin expression. These results can be the base of further studies to analyze the possible participation of PXR in the skeletal muscle effects shown by EC.

Online negative sentiment towards Mexicans and Hispanics and impact on mental well-being: A time-series analysis of social media data during the 2016 United States presidential election.

PURPOSE: The purpose was to use Twitter to conduct online surveillance of negative sentiment towards Mexicans and Hispanics during the 2016 United States presidential election, and to examine its relationship with mental well-being in this targeted group at the population level. METHODS: Tweets containing the terms Mexican(s) and Hispanic(s) were collected within a 20-week period of the 2016 United States presidential election (November 9th 2016). Sentiment analysis was used to capture percent negative tweets. A time series lag regression model was used to examine the association between percent count of negative tweets mentioning Mexicans and Hispanics and percent count of worry among Hispanic Gallup poll respondents. RESULTS: Of 2,809,641 tweets containing terms Mexican(s) and Hispanic(s), 687,291 tweets were negative. Among 8,314 Hispanic Gallup respondents, a mean of 33.5% responded to be worried on a daily basis. A significant lead time of 1 week was observed, showing that negative tweets mentioning Mexicans and Hispanics appeared to forecast daily worry among Hispanics by 1 week. CONCLUSION: Surveillance of online negative sentiment towards racially vulnerable population groups can be captured using social media. This has potential to identify early warning signals for symptoms of mental well-being among targeted groups at the population level.

Fleas of mammals and patterns of distributional congruence in northwestern Argentina: A preliminary biogeographic analysis.

In few groups of parasites have the patterns of distribution been studied using quantitative methods, even though, the study of these organisms indirectly provides information on the biogeographic history of their hosts, and in turn, the history of the hosts allows elucidation of speciation events of the parasites. Our objective was to quantitatively identify distributional congruence patterns of native fleas in northwestern Argentina. We analyzed 159 georeferenced distributional records of 47 species and six subspecies of fleas in northwestern Argentina using NDM/VNDM software. We found eight consensus areas, defined by 17 species and two subspecies, included in six patterns of distributional congruence (PDCs) with endemic and non-endemic fleas. The PDCs with the greatest values of endemicity (E) were mainly associated with Monte and Yungas Forests areas. All patterns indicated strong tendency of the Yungas Forests as a possible endemism area. Our results indicate that distributional congruence centers are generally located in Yungas Forests areas and highlight the importance of these areas in conservation and historical biology. This new information will allow delimitation of areas in the region at a more detailed resolution in the future.

Assessment of supervised classifiers for the task of detecting messages with suicidal ideation.

According to the World Health Organization (WHO) close to 800,000 people worldwide die by suicide each year, and many more attempts to do it. In consequence, the WHO recognizes suicide as a global public health priority, which affects not only rich countries but poor and middle-income countries as well. This study makes a systematic analysis of 28 supervised classifiers using different features of the corpus Life to detect messages with suicidal ideation and depression to know if these can be used in an automatic prevention online system. The Life Corpus, used in this research, is a bilingual text corpus (English and Spanish) oriented to the detection of suicide ideation. This corpus was constructed retrieving texts from several social networks and its quality was measured using mutual annotation agreement. The different experiments determined that the classifier with the best performance was KStar, with the corpus features POS-SYNSETS-NUM, achieving the best results with the ROC Area metrics of 0,81036 and F-measure of 0,7148. The present research fulfilled the objective of discovering which supervised classifiers and which features are the most suitable for the automatic classification of messages with suicidal ideation using the Life Corpus. Also, given the imbalance of the results, a new precision measure was developed called the Two-dimensional Accuracy and Recovery Index (GDP), which can provide better results, in unbalanced systems, than the usual measures to assess the quality of the results (measure F, Area ROC), and thus increase the number of messages at risk of suicidal ideation, detected at the cost of receiving more messages that are not related to suicide or vice versa.

Development of a new educational package based on e-learning to study engineering thermodynamics process: combustion, energy and entropy analysis.

This paper presents a new educational package based on e-learning called TermolabUA integrated by three programs, which are VOLCONTROL focused on the analysis of steady-state flow devices, CarnotCycle aimed to analyze reversible and irreversible processes, and CombustionUA to study combustion processes. The educational package was designed for both, to promote significant learning on some thermodynamic topics in undergraduate students, and to help the student to reach the cognitive competencies of interpreting, arguing and proposing, and interacting with the different graphical user interfaces to solve relevant cases studies. Also, the teaching-learning activity helps them to understand the influence of a specific variable on the energy and entropy behavior of the selected systems, which is traditionally studied manually in a classroom. The results of the t-Student tests showed that the average grades obtained by the students in the problems using the software were higher than the average grade without using the software. The estimate for the average grade difference was 0.56 with a P-value = 3.31E-13 for Problem 1 and 0.631 with a P-value = 3.31E-13 for Problem 2 in the Workshop- VOLCONTROL. Similar results were obtained for the problems reported in the CarnotCycle and CombustionUA Workshop with an estimate for average grade differences and P-values lower than 0.79 and 0.05, respectively. It means that the new software package significantly improved the learning skills of the students.

Flexible robotic teleoperation architecture for intelligent oil fields.

Teleoperation virtual platforms allow people to send their skills and capacities into machines located in either relative close (few meters away) or far (different continents) locations. With the use of lightweight protocols, people can remotely control the actions and movements of robots so they can avoid physical interaction with dangerous or risky places. Oil and gas well-pads stations are working zones considered hazardous due to the various chemical substances used in their daily processes. This characteristic makes these places the perfect candidates for the implementation of teleoperation solutions in order to reduce the direct interaction of humans with different chemicals and risky situations. The following investigation focuses on the development of a base teleoperation scheme to perform inspection and maintenance tasks in the inside one of these hydrocarbon facilities. The proposed system aims to generate an easily scalable teleoperation solution using distributed control schemes and a lightweight communication protocol to remotely manipulate a KUKA mobile manipulator. As the first stage of this investigation, the main result focuses on the development of the generic control and communication functions that allow the physical testing of the system using a KUKA YouBOT mobile manipulator and the help of a qualified operator of the station.

Fog computing at industrial level, architecture, latency, energy, and security: A review.

The industrial applications in the cloud do not meet the requirements of low latency and reliability since variables must be continuously monitored. For this reason, industrial internet of things (IIoT) is a challenge for the current infrastructure because it generates a large amount of data making cloud computing reach the edge and become fog computing (FC). FC can be considered as a new component of Industry 4.0, which aims to solve the problem of big data, reduce energy consumption in industrial sensor networks, improve the security, processing and storage real-time data. It is a promising growing paradigm that offers new opportunities and challenges, beside the ones inherited from cloud computing, which requires a new heterogeneous architecture to improve the network capacity for delivering edge services, that is, providing computing resources closer to the end user. The purpose of this research is to show a systematic review of the most recent studies about the architecture, security, latency, and energy consumption that FC presents at industrial level and thus provide an overview of the current characteristics and challenges of this new technology.

A multi-objective approach for designing optimized operation sequence on binary image processing.

In binary image segmentation, the choice of the order of the operation sequence may yield to suboptimal results. In this work, we propose to tackle the associated optimization problem via multi-objective approach. Given the original image, in combination with a list of morphological, logical and stacking operations, the goal is to obtain the ideal output at the lowest computational cost. We compared the performance of two Multi-objective Evolutionary Algorithms (MOEAs): the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). NSGA-II has better results in most cases, but the difference does not reach statistical significance. The results show that the similarity measure and the computational cost are objective functions in conflict, while the number of operations available and type of input images impact on the quality of Pareto set.

Random access with a distributed Bitmap Join Index for Star Joins.

Indices improve the performance of relational databases, especially on queries that return a small portion of the data (i.e., low-selectivity queries). Star joins are particularly expensive operations that commonly rely on indices for improved performance at scale. The development and support of index-based solutions for Star Joins are still at very early stages. To address this gap, we propose a distributed Bitmap Join Index (dBJI) and a framework-agnostic strategy to solve join predicates in linear time. For empirical analysis, we used common Hadoop technologies (e.g., HBase and Spark) to show that dBJI significantly outperforms full scan approaches by a factor between 59% and 88% in queries with low selectivity from the Star Schema Benchmark (SSB). Thus, distributed indices may significantly enhance low-selectivity query performance even in very large databases.

Effect of temperature on the formation of acrylamide in cocoa beans during drying treatment: An experimental and computational study.

The aim of this work was to determine the effect of temperature on the formation of acrylamide in cocoa beans during drying treatment by an experimental and computational study, in order to assess the presence of this neoformed compound from postharvest stage. The computational study was conducted on the reaction between fructose, glyoxal from glucose, and on asparagine at the M06-2X/6-31+G(d,p) level, under cocoa bean drying conditions at 323.15 to 343.15 K. The proposed reaction for acrylamide formation consisted of seven steps, which required to progress a via cyclic transition state of the four members. In addition, step III (decarboxylation) was considered to be the rate-determining step. Glucose followed an E1-like elimination and fructose exhibited an E1cb-like elimination. Computational model showed that the reaction of acrylamide formation was favored by fructose rather than glucose. The content of reducing sugars, asparagine and acrylamide in fermented and dried cocoa from two subregions of Antioquia-Colombia, as well as roasted cocoa, were evaluated by UHPLC-C-CAD and UHPLC-QqQ. The concentrations of monosaccharides measured at the end of the fermentation and drying process of cocoa nibs showed greater decreases in the levels of fructose as compared to glucose, supporting the main model hypothesis. Acrylamide formation only occurred in Bajo Cauca due to the presence of both precursors and fast drying time (72 h). Finally, it was possible to find the conditions to which acrylamide can be formed from the drying process and not only from roasting, information that can be used for future control strategies.

Adaptive learning objects in the context of eco-connectivist communities using learning analytics.

Eco-connectivist communities are groups of individuals with similar characteristics, which emerge in a connectivist learning process within a knowledge ecology. ARMAGAeco-c is a reflexive and autonomic middleware for the management and optimization of eco-connectivist knowledge ecologies using description, prediction and prescription models. Adaptive Learning Objects are autonomic components that seek to personalize Learning Objects according to certain contextual information, such as learning styles of the learner's, technological restrictions, among other aspects. MALO is a system that allows the management of Adaptive Learning Objects. One of the main challenges of the connectivist learning process is the adaptation of the educational context to the student needs. One of them is the learning objects. For this reason, this work has two objectives, specifying a data analytics task to determine the learning style of a student in an eco-connectivist community and, adapting instances of Adaptive Learning Objects using the learning styles of the students in the communities. We use graph theory to identify the referential member of each eco-connectivist community, and a learning paradigm detection algorithm to identify the set of activities, strategies, and tools that Adaptive Learning Objects instances should have, according to the learning style of the referential member. To test our approach, a case study is presented, which demonstrates the validity of our approach.

Sleep-wake stages classification using heart rate signals from pulse oximetry.

The most important index of obstructive sleep apnea/hypopnea syndrome (OSAHS) is the apnea/hyponea index (AHI). The AHI is the number of apnea/hypopnea events per hour of sleep. Algorithms for the screening of OSAHS from pulse oximetry estimate an approximation to AHI counting the desaturation events without consider the sleep stage of the patient. This paper presents an automatic system to determine if a patient is awake or asleep using heart rate (HR) signals provided by pulse oximetry. In this study, 70 features are estimated using entropy and complexity measures, frequency domain and time-scale domain methods, and classical statistics. The dimension of feature space is reduced from 70 to 40 using three different schemes based on forward feature selection with support vector machine and feature importance with random forest. The algorithms were designed, trained and tested with 5000 patients from the Sleep Heart Health Study database. In the test stage, 10-fold cross validation method was applied obtaining performances up to 85.2% accuracy, 88.3% specificity, 79.0% sensitivity, 67.0% positive predictive value, and 91.3% negative predictive value. The results are encouraging, showing the possibility of using HR signals obtained from the same oximeter to determine the sleep stage of the patient, and thus potentially improving the estimation of AHI based on only pulse oximetry.

S(-) and R(+) species derived from antihistaminic promethazine agent: structural and vibrational studies.

Structural and vibrational properties of free base, cationic and hydrochloride species derived from both S(-) and R(+) enantiomers of antihistaminic promethazine (PTZ) agent have been theoretically evaluated in gas phase and in aqueous solution by using the hybrid B3LYP/6-31G* calculations. The initial structures of S(-) and R(+) enantiomers of hydrochloride PTZ were those polymorphic forms 1 and 2 experimentally determined by X-ray diffraction. Here, all structures in aqueous solution were optimized at the same level of theory by using the polarized continuum (PCM) and the universal solvation model. As was experimentally reported, variations in the unit cell lead to slight energy, density, and melting point differences between the two forms but, this behavior is not carried through in isotropic condition, like in solution with non-chiral solvents. Hence, the N-C distances, Mulliken, atomic natural population (NPA) and Merz-Kollman (MK) charges, bond orders, stabilization and solvation energies, frontier orbitals, some descriptors and their topological properties were compared with the antihistaminic cyclizine agent. The frontier orbitals studies show that the free base species of both forms in solution are more reactive than cyclizine. Higher electrophilicity indexes are observed in the cationic and hydrochloride species of PTZ than cyclizine while the cationic species of cyclizine have higher nucleophilicity index than both species of PTZ. The presences of bands attributed to cationic species of both enantiomers are clearly supported by the infrared and Raman spectra in the solid phase. The expected 114, 117 and 120 vibration normal modes for the free base, cationic and hydrochloride species of both forms were completely assigned and the force constants reported. Reasonable concordances among the predicted infrared, Raman, UV-Vis and Electronic Circular Dichroism (ECD) with the corresponding experimental ones were found.

Big data in multi-block data analysis: An approach to parallelizing Partial Least Squares Mode B algorithm.

Partial Least Squares (PLS) Mode B is a multi-block method and a tightly coupled algorithm for estimating structural equation models (SEMs). Describing key aspects of parallel computing, we approach the parallelization of the PLS Mode B algorithm to operate on large distributed data. We show the scalability and performance of the algorithm at a very fine-grained level thanks to the versatility of pbdR, a R-project library for parallel computing. We vary several factors under different data distribution schemes in a supercomputing environment. Shorter elapsed times are obtained for the square-blocking factor 16 × 16 using a grid of processors as square as possible and non-square blocking factors 1000 × 4 and 10000 × 4 using an one-column grid of processors. Depending on the configuration, distributing data in a larger number of cores allows reaching speedups of up to 121 over the CPU implementation. Moreover, we show that SEMs can be estimated with big data sets using current state-of-the-art algorithms for multi-block data analysis.

Short-term wind speed forecasting in Uruguay using computational intelligence.

Short-term wind speed forecasting for Colonia Eulacio, Soriano Department, Uruguay, is performed by applying an artificial neural network (ANN) technique to the hourly time series representative of the site. To train the ANN and validate the technique, data for one year are collected by one tower, with anemometers installed at heights of 101.8, 81.8, 25.7, and 10.0 m. Different ANN configurations are applied for each site and height; then, a quantitative analysis is conducted, and the statistical results are evaluated to select the configuration that best predicts the real data. This method has lower computational costs than other techniques, such as numerical modelling. For integrating wind power into existing grid systems, accurate short-term wind speed forecasting is fundamental. Therefore, the proposed short-term wind speed forecasting method is an important scientific contribution for reliable large-scale wind power forecasting and integration in Uruguay. The results of the short-term wind speed forecasting showed good accuracy at all the anemometer heights tested, suggesting that the method is a powerful tool that can help the Administración Nacional de Usinas y Transmissiones Eléctricas manage the national energy supply.

Pedestrian counting estimation based on fractal dimension.

Counting the number of pedestrians in urban environments has become an area of interest over the past few years. Its applications include studies to control vehicular traffic lights, urban planning, market studies, and detection of abnormal behaviors. However, these tasks require the use of intelligent algorithms of high computational demand that need to be trained in the environment being studied. This article presents a novel method to estimate pedestrian flow in uncontrolled environments by using the fractal dimension measured through the box-counting algorithm, which does not require the use of image pre-processing and intelligent algorithms. Four scenarios were used to validate the method presented in this article, of which the last scene was a low-light surveillance video, showing experimental results with a mean relative error of 4.92% when counting pedestrians. After comparing the results with other techniques that depend on intelligent algorithms, we can confirm that this method achieves improved performance in the estimation of pedestrian traffic.

Evaluación final de Informática Médica para la formación de habilidades investigativas en estudiantes de medicina / Final evaluation of medical computer science for the training on investigative skills in medicine students

Se realizó un estudio descriptivo en la Facultad de Ciencias Médicas de Holguín, desde enero del 2017 hasta igual periodo del 2018, con vistas a caracterizar el proceso de evaluación final de la disciplina Informática Médica correspondiente al plan de estudio D para el desarrollo de habilidades investigativas en los estudiantes de medicina. El universo estuvo constituido por los resultados del trabajo final de las asignaturas Informática, Bioestadística y Metodología; la muestra, por los 40 trabajos finales de Metodología de la Investigación que tuvieron mayores dificultades en el informe escrito y 16 profesores de Informática Médica. Se utilizó la revisión documental, la observación y la entrevista. Se calcularon valores absolutos y porcentajes. Los resultados del trabajo final mostraron excelente promoción. El indicador defensa resultó el de mayor dificultad con 60,0 por ciento de los trabajos evaluados de regular y mal. La evaluación final contribuyó al desarrollo de la habilidad investigativa.

A descriptive study was carried out in Holguín Medical Sciences Faculty, from January, 2017 to the same period in 2018, with the aim of characterizing the process of final evaluation of the Medical Computer discipline corresponding to the study plan D for the development of investigative skills in the medicine students. The universe was constituted by the results of the final work of the subjects Computer science, Biostatistics and Methodology; the sample was constituted by the 40 final works of Methodology of Investigation which had the greatest difficulties in the written report and by 16 professors of Medical Computer science. The documental review, observation and interview were used. Absolute values and percentages were calculated. The results of the final work showed excellent promotion. The indicator defense was that of greatest difficulty with 60.0 percent of the evaluated works as average and poor. The final evaluation contributed to the development of the investigative skill.