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Here we consider a new approach to solving a one-stage optimization problem which is based on transformation of chance constraints into deterministic ones.
This approach is based on transformation of chance constraints into deterministic ones.
The first is the Short Time series Expression Miner (STEM) algorithm and software developed by Ernst et al., where all genes are clustered into one of a set of pre-defined patterns based on transformation of gene profiles into "units of change" [ 23].
The transient gene expression assay [ 6, 11, 12, 18] is based on transformation of single leaf epidermal cells, followed by subsequent inoculation of transformed leaves with specific powdery mildew isolates.
The method is based on transformation of collections of output functions into state variables.
The method is based on transformation of FSM structure table and replacement of some logical conditions.
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Alternatives based on transformations of the model have been suggested ([15, 50]) but, as reported in [33], they fall short when compared to SMC methods.
Casdagli et al. [35] give a thorough treatment of such techniques based on transformations of univariate maps, showing how optimal noise reduction can be achieved.
The most advanced ones are based on the transformation of vertical features into horizontal features and allow the preparation of lines with controlled width of 10 20 nm.
Their process is based on the transformation of a range of low-cost plant-based sugars into high-value oils using genetically transformed microalgae, initially into three target markets: fuels and chemicals, nutrition, and skin and personal care [48].
This method is based on the transformation of a set of assembly sequences into an hypergraph including "and" and "or " precedence relations between assembly tasks.
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