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During the past decades a number of dynamic tests have been used to dynamically monitor the changes in stroke volume after a maneuver that modifies venous return.
In this case, to dynamically monitor the rainfall intensity, physical properties of rainfall like spatial sparsity and non-negativity can be exploited as extra information.
It would be more valuable if we re-define, re-categorize, and discover diseases, as well as dynamically monitor therapeutic efficacy on basis of clinical lipidomics.
In this section, we present some simulation results to test the developed methodologies to dynamically monitor the rainfall in a given area.
We develop a complete structured framework to dynamically monitor the rainfall intensity exploiting a priori knowledge, i.e., the spatial sparsity and the non-negativity of the rainfall field.
These pathways dynamically monitor cellular status, and mediate adaptive changes by remodeling the proteome, largely accomplished by remodeling transcriptional networks and protein degradation.
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This is illustrated for one specific application scenario where network conditions are dynamically monitored.
In the DST-LACK scheme, the IR is dynamically monitored and adjusted based on automatic feedback quality control.
A sample application for dynamically monitoring and controlling remote facilities illustrates the benefits of this approach to virtual manufacturing.
The system dynamically monitors the CPU processing and its utilization and periodically applies appropriate ACPI's P-states (Wei et al. 2009).
Then the daily SOC information of each PrEV is available, and the overall SOC distribution of the PrEV cluster is dynamically monitored and saved.
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