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By examining three developmental processes and corresponding mathematical models, this Review addresses the potential of mathematical modelling to help understand development.
Based on data imputation principles, together with several mathematical models, this paper presents a CCDSS, the Patient Oriented Method of Pain Evaluation System (POMPES), that produces tailored alarms, reports, and clinical guidance based on collected patient-reported data.
Since most nonlinear systems are complicated to establish accurate mathematical models, this paper provides a novel data-based approximate optimal control algorithm, named iterative neural dynamic programming (INDP) for affine and non-affine nonlinear systems by using system data rather than accurate system models.
Methods from multivariate data analysis are powerful tools for analyzing such data and for creating mathematical models; this is the essential part of today's chemometrics [1].
When combined with mathematical models, this information provides further insight into underlying mechanisms and important parameters that are unable to be extracted from experimental techniques.
According to mathematical models, this might lead to a substantial increase in HZ-incidence in the population for 40 to 50 years after the initiation of routine varicella vaccination [ 43, 44].
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Here we perform a rapid analysis of the mathematical model, this type of model having been studied in more detail elsewhere [28], [31].
In our mathematical model this means a free choice in some parameter values within given intervals.
Instead of mathematical modeling, this problem can be approached more fruitfully from a different dimension, taking advantage of the enormous power of contemporary supercomputers.
When theoretical physicists attempt to create mathematical models of this kind of universe, they find something interesting.
The existing mathematical models of this phenomenon are, unfortunately, rather unreliable.
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