{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:47:15Z","timestamp":1760143635851,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,24]],"date-time":"2024-02-24T00:00:00Z","timestamp":1708732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Brazilian National Council for Scientific and Technological Development-CNPq","award":["306607\/2023-9"],"award-info":[{"award-number":["306607\/2023-9"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Respiratory diseases are among the leading causes of death globally, with the COVID-19 pandemic serving as a prominent example. Issues such as infections affect a large population and, depending on the mode of transmission, can rapidly spread worldwide, impacting thousands of individuals. These diseases manifest in mild and severe forms, with severely affected patients requiring ventilatory support. The air\u2013oxygen blender is a critical component of mechanical ventilators, responsible for mixing air and oxygen in precise proportions to ensure a constant supply. The most commonly used version of this equipment is the analog model, which faces several challenges. These include a lack of precision in adjustments and the inspiratory fraction of oxygen, as well as gas wastage from cylinders as pressure decreases. The research proposes a blender model utilizing only dynamic pressure sensors to calculate oxygen saturation, based on Bernoulli\u2019s equation. The model underwent validation through simulation, revealing a linear relationship between pressures and oxygen saturation up to a mixture outlet pressure of 500 cmH2O. Beyond this value, the relationship begins to exhibit non-linearities. However, these non-linearities can be mitigated through a calibration algorithm that adjusts the mathematical model. This research represents a relevant advancement in the field, addressing the scarcity of work focused on this essential equipment crucial for saving lives.<\/jats:p>","DOI":"10.3390\/s24051481","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T03:34:04Z","timestamp":1708918444000},"page":"1481","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A New Model of Air\u2013Oxygen Blender for Mechanical Ventilators Using Dynamic Pressure Sensors"],"prefix":"10.3390","volume":"24","author":[{"given":"Gabryel F.","family":"Soares","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Piau\u00ed (UFPI), Teresina 64049-550, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4498-2427","authenticated-orcid":false,"suffix":"Jr.","given":"Gilberto","family":"Fernandes","sequence":"additional","affiliation":[{"name":"Computer Science Department, State University of Londrina (UEL), Londrina 86057-970, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6950-2891","authenticated-orcid":false,"given":"Otac\u00edlio M.","family":"Almeida","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Federal University of Piau\u00ed (UFPI), Teresina 64049-550, Brazil"}]},{"given":"Gildario D.","family":"Lima","sequence":"additional","affiliation":[{"name":"Federal University of Delta do Parna\u00edba (UFDPar), Parna\u00edba 64202-020, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8657-3800","authenticated-orcid":false,"given":"Joel J. P. C.","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"COPELABS, Lus\u00f3fona University, 1749-024 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Marcassoli, A., Leonardi, M., Passavanti, M., De Angelis, V., Bentivegna, E., Martelletti, P., and Raggi, A. (2023). Lessons Learned from the Lessons Learned in Public Health during the First Years of COVID-19 Pandemic. Int. J. Environ. Res. 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