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The framework considers also the validation and generalisation of the system.
The framework considers procurement of feedstocks, plantation of energy crops, and different modes for transportation of feedstocks and final products.
The framework considers soil moisture as a response variable and several factors, such as precipitation, soil properties, and temperature, as explanatory variables.
The framework considers objectives related to locational agreement and landscape pattern structure, as well as the inherent stochasticity of land-use models.
More precisely, the framework considers triangular, interval, and numeric types of data to construct granular fuzzy models as particular arrangements of trapezoids.
The framework considers contract types and retainage to estimate cash inflow, and cash outflow patterns for equipment, manpower, and materials in order to more accurately measure cash outflow.
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In the framework considered here, returns are reduced for some farmers and left unaffected for others.
We survey the measurement stage of the framework, considering methods that rely on bitstream, pixels, or both.
Note that whereas most prior work on sparse approximation focuses on static scenarios, the framework considered in this article addresses the problem of learning in dynamical systems.
The framework considered assumes that the economic function is a known function of constrained system's states, parameterized by unknown parameters.
The genes embedded in these regions showed a dynamic and autonomous regulation during mouse intestinal cell differentiation, indicating that, in the framework considered here, the coordinated regulation in LRES is restricted to cancer.
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