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iCFD tools could play a crucial role in reliably predicting systems' performance under normal and shock events.
The utilities of such modeling include predicting systems behavior, identifying crucial steps in systems regulation.
As mentioned above the potential of outcome predicting systems concerning the judgment about an individual therapy may be limited.
While the main hallmark of systems biology is often portrayed as a means for predicting systems responses to external or internal stimuli, a similarly important aspect is its abilities to envisage all relevant information and integrate experimental data into logical and computable structures.
Furthermore, it should be noted that although ICU outcome predicting systems are very important statistical tools for quality management or clinical trials (i.e. in implementing stratification or for clustering) in several cases their potential concerning judgment about an individual therapy may be limited.
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ANN has been viewed as a useful tool for managers in predicting system behaviors.
Also, the sensors' data are useful for predicting system operation and network failures.
These characteristics make frequency response methods particularly suitable to characterizing and predicting system response to transients.
In order to quantify the robustness of the proposed metric in predicting system performance, we need first to define the quality sets of the used biometric authentication systems.
These data is important for system simulations and development of health monitoring algorithms predicting system behaviour with faults.
However, existing reliability modelling approaches lack any methodologies to take account of maintenance actions in predicting system failure probability.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com