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Mass-spring simulation with proposed method showed improved performance.
The proposed method showed improved performance compared to the other method[31] (http://www.na.icar.cnr.it/~maddalena.l/MODLab/SoftwareSOBS.html).l/MODLab/SoftwareSOBS.html
Therefore, in the WS and the FT sequences that contained objects whose intensity values were similar to the background regions, the proposed method showed improved performance compared to the other methods.
The formation of pGPPCN/TiO2 donor-acceptor heterojunction prepared by this method showed improved surface area and light absorption.
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The proposed method shows improved tracking performance compared with the existing model free controller in co-simulations.
The comparisons revealed that the proposed method shows improved discharge calculation within 3.8percentt absolute mean error while the traditional method has more than 12.5percentt error.
The method shows improved numerical accuracy and stability properties compared to the direct implicit method with a first-order integration scheme.
In the table, the probabilistic 2-mode based method shows improved performance over the Tucker decomposition based method for small amounts of adaptation data, which can be evidently seen in Figure6 for the Tucker decomposition and probabilistic 2-mode based models with (K R =20,K D =35).
The new method shows improved robustness, accuracy, and power compared to the existing method, msBayes.
Compared to the previous normalization, the method shows improved results for inferring differential expression in simulated and real data.
Through comprehensive simulations, our method shows improved results for DE expression, in terms of false discovery rate (FDR), and sensitivity and specificity, in RNA-Seq experiments.
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