Because of machine learning's widespread applications, there exists a plethora of tools that empower its implementation.
Unfortunately, such an approach is corrupted by model uncertainty due to the fact that in real applications there exists a model reality mismatch.
Yet among these applications there exists a heterogeneous understanding about what virtual patients are and for what purposes they may be used.
Since these implantable sensors are well positioned to further advance our general understanding of brain function, especially for clinical and longitudinal applications, there exists an urgent need to understand the tissue response and its impact on the outcome of neurochemical and physiological recordings.
In many geophysical fluid modeling applications there exist two very different time scales, essentially fast waves and slow vortices.
Nevertheless, in manufacturing applications there exist also aperiodic traffic (events and alarms) for which the Fieldbus must provide guaranteed levels of QoS at application run-time.
In practical applications, there exist unavoidably estimation errors of the CSI, which results in outage of quality of service (QoS) or overconsumption of transmit power.
In practical applications, there exist many types of time delays such as discrete delays [3], time-varying delays [4], distributed delays [5, 6], random delays [7] and leakage delays (or forgetting delays) [8, 9].
However, in many applications, there exist groups of signals of interest, where components from different groups are mutually statistically independent indeed, but where mutual statistical dependence occurs between components in the same group.
Therefore, despite their great utility in many applications, there exist disadvantages to draft genome sequencing that need to be addressed by appropriate experimental design and deployment of sequencing resources to effectively answer the study questions posed.
