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The education sector was considerably affected by the COVID-19 pandemic, risking the learning process and forcing governments to pull out contingency actions to ensure the students' development of generic and specific skills and guarantee education quality. These actions include the shift to online and hybrid (i.e., online and in-person) classes. This work assesses the pandemic effect on the generic skills of undergraduate students in a Colombian university. The study aims to determine the effect of COVID- 19 on the development of generic skills quantitatively. Two datasets were retrieved: i) A dataset with the scores obtained by the students in an institutional Generic Skills test and in the midterm tests; ii) A dataset with the students' scores in the Colombian standardized test for undergraduate students, called the Saber Pro test. Three analysis stages were performed: i) Univariate exploratory analysis; ii) Differential analysis to compare the No COVID vs. COVID scenarios; iii) A correlation analysis. Results showed that the scores of the Generic Skills and the Midterm tests increased significantly when comparing the two scenarios, except for the Written Communication. As for the Saber Pro test, only the scores for Written Comprehension, Quantitative Reasoning, and the global scores increased significantly. On the other hand, the correlation analysis showed a strong correlation only between the scores obtained at the Generic Skills and the Saber Pro tests for the English Proficiency skill. In addition, the analysis elucidated a weak correlation between the Generic Skills test's average and the Saber Pro's global score. Finally, the results prove that online education is a feasible alternative that offers students more flexibility to ensure the development of generic and specific skills.
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The throughput of a finite-capacity queueing system is the mean number of clients served during a time interval. The COVID-19 outbreak has posed a serious challenge for many commercial establishments, including the retails, which have struggled to adapt to new working dynamics. Retails have been forced to adjust their service guidelines to comply with biosecurity protocols, ensuring to observe governmental and public health policies. A significant change for the retail market has been the capacity restrictions to ensure social distancing, i.e., a limitation on the number of customers simultaneously shopping in the store. Such a constraint has an impact on the throughput that can be achieved by a retail. This article assesses the impact of the capacity restriction measures on an Amazon Go-like retail performance through a throughput analysis under COVID-19-related capacity restrictions. For the assessment, we first retrieved real data from a retail located in Cartagena, Colombia. Two scenarios were considered: i) low demand and ii) high demand. Further, we built an Amazon Go-like, two-queue, M/M/c/K retail model with a CONWIP (Constant Work-In-Process) approach, considering biosecurity-based capacity restrictions due to the COVID-19 pandemic. The R package 'queueing' was used to set up the model, and an algorithm was created to go over each sampling period and find the hourly optimum capacity and throughput under the dynamic conditions of both scenarios (low and high demand). Results from the performance analysis show that, for some operational conditions, the optimum maximum throughput is achieved with capacities below the biosecurity-based capacity, while for some other operational conditions the maximum throughput cannot be achieved with the restrictions, as the optimum capacity lies beyond the biosecurity-based capacity. These results suggest that the maximum capacity definition should not be static. Instead, it should be done considering the retail's dimensions, the biosecurity policies, and the dynamic retail's operational conditions such as the demand and service capacity.
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BACKGROUND: High-throughput sequencing enables the analysis of the composition of numerous biological systems, such as microbial communities. The identification of dependencies within these systems requires the analysis and assimilation of the underlying interaction patterns between all the variables that make up that system. However, this task poses a challenge when considering the compositional nature of the data coming from DNA-sequencing experiments because traditional interaction metrics (e.g., correlation) produce unreliable results when analyzing relative fractions instead of absolute abundances. The compositionality-associated challenges extend to the classification task, as it usually involves the characterization of the interactions between the principal descriptive variables of the datasets. The classification of new samples/patients into binary categories corresponding to dissimilar biological settings or phenotypes (e.g., control and cases) could help researchers in the development of treatments/drugs. RESULTS: Here, we develop and exemplify a new approach, applicable to compositional data, for the classification of new samples into two groups with different biological settings. We propose a new metric to characterize and quantify the overall correlation structure deviation between these groups and a technique for dimensionality reduction to facilitate graphical representation. We conduct simulation experiments with synthetic data to assess the proposed method's classification accuracy. Moreover, we illustrate the performance of the proposed approach using Operational Taxonomic Unit (OTU) count tables obtained through 16S rRNA gene sequencing data from two microbiota experiments. Also, compare our method's performance with that of two state-of-the-art methods. CONCLUSIONS: Simulation experiments show that our method achieves a classification accuracy equal to or greater than 98% when using synthetic data. Finally, our method outperforms the other classification methods with real datasets from gene sequencing experiments.
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The study presents a complete one-dimensional model to evaluate the parameters that describe the operation of a Proton Exchange Membrane (PEM) electrolyzer and PEM fuel cell. The mathematical modeling is implemented in Matlab/Simulink® software to evaluate the influence of parameters such as temperature, pressure, and overpotentials on the overall performance. The models are further merged into an integrated electrolyzer-fuel cell system for electrical power generation. The operational description of the integrated system focuses on estimating the overall efficiency as a novel indicator. Additionally, the study presents an economic assessment to evaluate the cost-effectiveness based on different economic metrics such as capital cost, electricity cost, and payback period. The parametric analysis showed that as the temperature rises from 30 to 70 °C in both devices, the efficiency is improved between 5-20%. In contrast, pressure differences feature less relevance on the overall performance. Ohmic and activation overpotentials are highlighted for the highest impact on the generated and required voltage. Overall, the current density exhibited an inverse relation with the efficiency of both devices. The economic evaluation revealed that the integrated system can operate at variable load conditions while maintaining an electricity cost between 0.3-0.45 $/kWh. Also, the capital cost can be reduced up to 25% while operating at a low current density and maximum temperature. The payback period varies between 6-10 years for an operational temperature of 70 °C, which reinforces the viability of the system. Overall, hydrogen-powered systems stand as a promising technology to overcome energy transition as they provide robust operation from both energetic and economic viewpoints.
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HIV infection has a tremendous impact on the immune system's proper functioning. The mucosa-associated lymphoid tissue (MALT) is significantly disarrayed during HIV infection. Compositional changes in the gut microbiota might contribute to the mucosal barrier disruption, and consequently to microbial translocation. We performed an observational, cross-sectional study aimed at evaluating changes in the fecal microbiota of HIV-infected individuals from Colombia. We analyzed the fecal microbiota of 37 individuals via 16S rRNA gene sequencing; 25 HIV-infected patients and 12 control (non-infected) individuals, which were similar in body mass index, age, gender balance and socioeconomic status. To the best of our knowledge, no such studies have been conducted in Latin American countries. Given its compositional nature, microbiota data were normalized and transformed using Aitchison's Centered Log-Ratio. Overall, a change in the network structure in HIV-infected patients was revealed by using the SPIEC-EASI MB tool. Genera such as Blautia, Dorea, Yersinia, Escherichia-Shigella complex, Staphylococcus, and Bacteroides were highly relevant in HIV-infected individuals. Differential abundance analysis by both sparse Partial Least Square-Discriminant Analysis and Random Forest identified a greater abundance of Lachnospiraceae-OTU69, Blautia, Dorea, Roseburia, and Erysipelotrichaceae in HIV-infected individuals. We show here, for the first time, a predominantly Lachnospiraceae-based signature in HIV-infected individuals.