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Thin-film silicon tandem (MICROMORPH™) module design optimization process for performance maximization is described together with an analysis of some key reliability topics.
The heuristic method for throughput maximization is described in Section 3. The approximation algorithm is discussed in Section 4. Section 5 presents the simulation results.
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Optimal operation is described as maximization of the clean water produced while being a self sufficient entity in terms of energy.
The main task in machine learning problems is to find the optimal parameter (theta ) of the model given a training set D. In probabilistic perspective, it is described as a maximization of log likelihood function.
The system throughput maximization problem of the small cell network is described as: textbf{P1}:quadmaxsumlimits_{j=1}^{N}R_{j} (5).
However, when it is already known that the system is described by power law/Pareto distribution, it follows that these distributions can be obtained from maximization of the Tsallis q-entropy.
The system value-based sum capacity/throughput maximization methods for optimum signature sequence selection, energy allocation, and rate maximization methods will be described in Section 6.
The use of logistic regressions using the expectation maximization (EM) algorithm was described in detail elsewhere [ 42- 44].
Based on the mathematical formulation presented in Methods, the ICO problem can be described as the maximization of ICU fitness, ΨICU (see Eqn. 23), subject to the constraint that the codon sequence can be translated into the target protein (see Eqns. 3, 4 and 11).
In addition, we have applied the program "Multiple Expectation Maximization for Motif Elicitation" (MEME) [ 44] for the detection of short conserved sequence parts that have not been described yet as Pfam and/or SMART motif.
In this case the maximization is observed in the linear regime (i.e. for small amplitude of the stimulations) and it can be described as a standard phenomenon of resonance.
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