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10.1002/mma.7443 http://scihub22266oqcxt.onion/10.1002/mma.7443 34230731!8250794!34230731     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Math+Methods+Appl+Sci 2021 ; 44 (13): 10762-10771 Nephropedia Template TP {{tp|p=34230731|t=2021. Developing a detection model for a COVID-19 infected person based on a probabilistic dynamical system |pdf=|usr=}}{{34230731}} gab.com Text Developing a detection model for a COVID-19 infected person based on a probabilistic dynamical system JO: Math Methods Appl Sci http://www.kidney.de/pget.php?&tw=1&pmid=34230731 #FOAvip This paper presents a novel model to detect the COVID-19 infected person from a Markovian feedback persons in a limited department capacity. The persons arrive one by one to the department and the balking and the retention of reneged person approaches are considered. There exists one server presents the service to these persons according to first-come, first-served (FCFS) discipline. An efficient and novel algorithm is presented to get the exact value of the probability of n persons in the department at any time interval. This algorithm depends on the Laplace transform to solve a probabilistic dynamical system of differential equations. By considering the exponential detection function and if the probability of the infected person in the department is equal to the probability of each one, then this algorithm is useful to obtain the detection probability of the infected one. Under steady state, the detection probability of the infected person is described. The usefulness of this model is illustrated for different capacities by using a numerical example to describe the behavior of probabilities of the persons in the department, the detection probabilities of the infected person as functions in time, and the mean time to detection. Twit Text FOAVip Developing a detection model for a COVID-19 infected person based on a probabilistic dynamical system ????Math Methods Appl Sci http://www.kidney.de/pget.php?&tw=1&pmid=34230731 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34230731 #FOAvip English Wikipedia <ref>{{Cite pmid|34230731}}</ref> Developing a detection model for a COVID-19 infected person based on a probabilistic dynamical system #MMPMID34230731 El-Hadidy MAA Math Methods Appl Sci 2021[Sep]; 44 (13): 10762-10771 PMID34230731show ga This paper presents a novel model to detect the COVID-19 infected person from a Markovian feedback persons in a limited department capacity. The persons arrive one by one to the department and the balking and the retention of reneged person approaches are considered. There exists one server presents the service to these persons according to first-come, first-served (FCFS) discipline. An efficient and novel algorithm is presented to get the exact value of the probability of n persons in the department at any time interval. This algorithm depends on the Laplace transform to solve a probabilistic dynamical system of differential equations. By considering the exponential detection function and if the probability of the infected person in the department is equal to the probability of each one, then this algorithm is useful to obtain the detection probability of the infected one. Under steady state, the detection probability of the infected person is described. The usefulness of this model is illustrated for different capacities by using a numerical example to describe the behavior of probabilities of the persons in the department, the detection probabilities of the infected person as functions in time, and the mean time to detection. äDeveloping a detection model for a COVID-19 infected person based on a(epmc).pdf DeepDyve Pubget Overpricing