Autonomous Mobile Robots (AMRs) are becoming pervasive and, in consequence, the optimization of their electronics is also becoming a current research topic.
As a consequence of the optimization of experimental conditions performed in laboratory, underwater characterization of ancient pottery was achieved.
We interpret this observation as a consequence of the optimization process that identifies core TFs responsible for the big alterations in transcriptomic profiles.
As a consequence, in clinical practice, the optimization of CRT is usually achieved with the assistance of non-invasive echocardiographic procedures, which can only be performed with the patient supine, and are costly, time-consuming, and operator-dependent.
As a consequence, the number of variables involved in the optimization in MI-1 SVM is much smaller than that in mi-SVM and this leads to faster training.
As a consequence, the optimization results have no practical meaning.
As a consequence, the optimization algorithm is not driven by the gradient of Ψ, but by a number of rule-based heuristics: If a macrostate has low predictability, it is split in two.
Productivity also results in the optimization of resources.
The way to work about this limitation, is to add the boundary conditions of equation (5) to the shape-optimization method, applied later in the optimization process, after the shape-optimization has been preliminarily compared to the topology-optimization.
For this reason, g is not added as an optimization variable in the optimization problem.
