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Most conventional parameter identifiability analysis approaches mainly rely on analytics.
Ontology-based approaches mainly support context awareness and integration of heterogeneous sensor networks.
Both approaches mainly base on Q-Learning and allow for identifying Pareto optimal solutions.
These estimation approaches mainly differ in the number of parameters the estimation is based on.
In contrast, previous approaches mainly relied on an implicit solution, requiring computationally intensive inner iterations.
These approaches mainly apply student experimental studies with incentives (gift reward).
As SISR essentially is a highly ill-posed problem, the performance of such approaches mainly relies on the prior knowledge.
However, these approaches mainly focused on the data of the earthquakes themselves and few exogenous factors were considered.
The aforementioned approaches mainly focus on providing different virtualization, management and (semantic-based) interoperability techniques for IoT devices.
Direct approaches mainly focus on global methylation analysis, while indirect approaches rely on specific recognition of DNA-methylation loci.
Contractual approaches, mainly derived from Transaction Costs Economics, offer some plausible explanations for reintegration originating from outsourcing failure.
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CEO of Professional Science Editing for Scientists @ prosciediting.com