COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level—SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.

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The performance of solar thermal technology under high-altitude equatorial climatic and solar path conditions has not been determined. Evacuated tube solar collectors are more efficient than flat plate collectors in cold and cloudy regions; however, due to their dependence on orientation, the irradiation incidence between the tubes of these collectors can be blocked. In this study, the performance of these types of collectors was analyzed to determine the implications of their orientation under these specific climate conditions. Four solar thermal systems were installed: two of the systems used evacuated tube collectors, and two used flat plate collectors. Each collector was connected to storage and discharge points to simulate residential consumption when observing the real performance of the four systems in terms of irradiation availability. The evacuated tube collectors were more efficient and reduced the backup energy requirement by up to 20.6% more on average than the flat plate collectors. In addition, the performance of the evacuated tube collectors increased by up to 9.8% when the tubes were arranged parallel to the solar path, compared to when they were arranged perpendicular to the solar path, verifying that the blockage effect is an important parameter to consider for evacuated tube technology. The main novelty of this research is the comparison of these two technologies under different orientations, with perpendicular and parallel dispositions toward the solar path, in a high-altitude equatorial location where solar collectors are not typically oriented in any particular orientation. To the best of our knowledge, this is the first complete analysis of real systems deployed under these conditions.

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Low-temperature solar thermal energy is a viable and mature alternative to reduce the use of fossil fuels for domestic hot water heating. The impact of the inclusion of this technology in the energy matrix in the Andean city of Cuenca, Ecuador, is analyzed. In Ecuador, liquefied petroleum gas (LPG) is currently used to heat water due to the high state subsidy, with a cost of 0, 11 USD for an LPG kg compared to the international price of 1.18 USD a Kg of LPG in 2021. Sustainability indicators show that the urban energy matrix would be minimally affected by a high penetration of solar thermal systems. The indicators that are positively affected are related to energy self-sufficiency, emissions, and employment at the expense of an increase in the cost of energy. Moreover, the analysis of the impact on consumption exclusively in the residential sector shows that liquefied petroleum gas consumption would drop from 73 to 64%, mainly because the liquefied gas is used in cooking. However, the change in technology is limited by the subsidized cost of liquefied petroleum gas, which makes the adoption of this technology difficult in the current state

In this work, residential solar systems were simulated to predict feeding domestic hot water, using TRNSYS (TraNsient SYstem Simulation software), under the operating conditions in Cuenca, Ecuador. The type of research is quantitative and with a correlational approach. Using data from an experimental test, a technical sheet was prepared that sought to identify the main variables to be applied in the model. The diffuse and direct radiation data were obtained by applying the isotropic diffuse model. When simulating in the software, the results show that the system presents an efficiency of 56.54% when heating water in a range of 30° to 45 °C. For the validation of the model, the chisquared statistical test was applied, with which it was determined that the simulated data follow the same distribution as the real data.

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