{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T00:38:19Z","timestamp":1698885499414},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684468","type":"print"},{"value":"9781643684475","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,30]]},"abstract":"<jats:p>Bayesian inference, a statistical methodology rooted in Bayes\u2019 theorem, offers the ability to compute probability distributions of unobserved phenomena, given observed information. To this end, this technique has proven useful in disease diagnosis based on equipment measurement. This paper proposes an innovative Bayesian inference strategy capable of rapidly estimating capillary oxygen supply capability in muscle tissues by leveraging uncertainty quantification techniques. Specifically, the oxygen supply capability is formulated with Krogh Erlang\u2019s equation along with Fick\u2019s second law. Moreover, the prior distribution of the early-time capillary oxygen supply capability is updated using acquired measurements of oxygen concentration within capillary to yield the posterior distribution. The resulting data with supportive simulation indicates that the cellular dimension can be efficiently updated, thereby facilitates the accurate uncertainty quantification of cellular environment estimate.<\/jats:p>","DOI":"10.3233\/faia230778","type":"book-chapter","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T11:02:52Z","timestamp":1698836572000},"source":"Crossref","is-referenced-by-count":0,"title":["Estimate of Capillary Oxygen Supply Capability Through Monte Carlo Simulation-Based Bayesian Inference"],"prefix":"10.3233","author":[{"given":"Jiaxuan","family":"Guo","sequence":"first","affiliation":[{"name":"Beijing Chaoyang Tongwen Foreign Language School, Beijing, 100024, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Machine Learning and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230778","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T11:03:02Z","timestamp":1698836582000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230778"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,30]]},"ISBN":["9781643684468","9781643684475"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230778","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,30]]}}}