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Special classes of asynchronous e-learning systems are the intelligent tutoring systems which represent an advanced learning and teaching environment adaptable to individual student's characteristics.
Continuous Medical Education (CME) sites dedicated in cytopathology field are rather poor, they do not succeed in following the constant changes and lack the ability of providing cytopathologists with a dynamic learning environment, adaptable to the development of cytopathology.
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Environment-Adaptable Fast optimization.
Environment-Adaptable Fast Multi-Resolution Optimization.
In order to efficiently find an optimal configuration, Environment-Adaptable Fast (EAF) optimization utilizes calculated signal-to-interference-and-noise ratio (SINR) and narrows down the searching space (Jun et al., Environment-adaptable efficient optimization for programming of reconfigurable Radio Frequency (RF) receivers, 2014).
In order to utilize the large-scale RF-FPGA for cognitive radio, our research focused on developing an efficient algorithm, called Environment-Adaptable Fast Multi-Resolution (EAF-MR) method.
Thus, a fast optimization algorithm called Environment-Adaptable Fast (EAF) optimization was proposed for speeding up the optimization of a reconfigurable RF front-end in a dynamic spectrum environment [9].
Using the estimated results, the SINR formula was calculated and used for the Environment-Adaptable Fast Multi-Resolution (EAF-MR) optimization, which was designed by applying the EAF optimization to multi-resolution optimization.
(2) environment-adaptable algorithm: when choosing an optimal configuration in a large-scale RF-FPGA, the EAR-MR algorithm reflects communication quality changes due to interference in the field.
Solving the defined optimization problem, we introduce the Environment-Adaptable Fast Multi-Resolution (EAF-MR) optimization designed for a large-scale RF-FPGA to adapt to the dynamic conditions in communication.
We identified a number of such regions, which form a complex network controlling seed isoflavone levels Overall, our results suggest that isoflavone accumulation in soybean seeds is controlled by a complex environment-adaptable network of interacting nodes.
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