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|a 9783642328824
|9 978-3-642-32882-4
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|a 10.1007/978-3-642-32882-4
|2 doi
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|2 bisacsh
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|a 570.285
|2 23
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|a Batzel, Jerry J.
|e editor.
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|a Mathematical Modeling and Validation in Physiology
|b Applications to the Cardiovascular and Respiratory Systems /
|c edited by Jerry J. Batzel, Mostafa Bachar, Franz Kappel.
|h [electronic resource] :
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| 264 |
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1 |
|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2013.
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| 300 |
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|a XX, 254 p. 83 illus., 34 illus. in color.
|b online resource.
|
| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a Lecture Notes in Mathematics,
|v 2064
|x 0075-8434 ;
|
| 505 |
0 |
# |
|a 1 Merging Mathematical and Physiological Knowledge: Dimensions and Challenges -- 2 Mathematical Modeling of Physiological Systems -- 3�Parameter Selection Methods in Inverse Problem Formulation.-�4 Application of the Unscented Kalman Filtering to Parameter Estimation -- 5 Integrative and Reductionist Approaches to Modeling of Control of Breathing -- 6 Parameter Identification in a Respiratory Control System Model with Delay -- 7 Experimental Studies of Respiration and Apnea -- 8 Model Validation and Control Issues in the Respiratory System -- 9 Experimental Studies of the Baroreflex -- 10 Development of Patient Specific Cardiovascular Models Predicting Dynamics in Response to Orthostatic Stress Challenges -- 11 Parameter Estimation of a Model for Baroreflex Control of Unstressed Volume.
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| 520 |
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|a This volume synthesizes theoretical and practical aspects of both the mathematical and life science�viewpoints needed for modeling of�the cardiovascular-respiratory system specifically and�physiological systems generally.� Theoretical points include model design,�model complexity and validation in the light of available data, as well as control theory approaches�to feedback delay�and Kalman filter applications to�parameter identification. State of the art approaches using parameter sensitivity are discussed for�enhancing model�identifiability through joint analysis of�model structure and data. Practical examples illustrate�model development at various levels of complexity based on given physiological information. The sensitivity-based approaches for examining model identifiability are illustrated by means of specific modeling� examples. The themes presented address the current problem of patient-specific model adaptation in the clinical setting, where data is typically limited.
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|a Mathematics.
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| 650 |
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|a Human physiology.
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| 650 |
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|a Biology
|x Data processing.
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| 650 |
1 |
4 |
|a Mathematics.
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| 650 |
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|a Mathematical and Computational Biology.
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| 650 |
2 |
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|a Human Physiology.
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| 650 |
2 |
4 |
|a Computer Appl. in Life Sciences.
|
| 700 |
1 |
# |
|a Bachar, Mostafa.
|e editor.
|
| 700 |
1 |
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|a Kappel, Franz.
|e editor.
|
| 710 |
2 |
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|a SpringerLink (Online service)
|
| 773 |
0 |
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|t Springer eBooks
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783642328817
|
| 830 |
# |
0 |
|a Lecture Notes in Mathematics,
|v 2064
|x 0075-8434 ;
|
| 856 |
4 |
0 |
|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-32882-4
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| 912 |
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|a ZDB-2-SMA
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| 912 |
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|a ZDB-2-LNM
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| 950 |
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|a Mathematics and Statistics (Springer-11649)
|