Sentence examples for discrepancy of the predicted from inspiring English sources

Open image in new window Fig. 8 Surface roughness for test data Open image in new window Fig. 9 The discrepancy of the predicted value from the experimental in percentage, for test data.

Open image in new window Fig. 3 MSE during training Open image in new window Fig. 4 Adaptive mu during training Open image in new window Fig. 5 Experimental values of surface roughness versus the NN predicted ones Open image in new window Fig. 6 The discrepancy of the predicted value from the experimental in percentage, for test data.

Figure 9 shows the discrepancies of the predicted values from the experimental ones in percentage, for the test data.

Particularly, the composite National Nosocomial Infection Surveillance NNISS) risk score was found to be positively correlated to the risk of inhospital SSI [ 18], whereas other reports have documented a discrepancy between the predicted risk of infection by NNIS index score and the incidence of postdischarge SSI [ 7, 8].

A comparison of test data collected at an existing ACC and numerical data generated in a CFD analysis of the flow around the same ACC shows a discrepancy in the predicted effects of wind on fan inlet temperature.

During characterization of this protein we observed a discrepancy from the predicted molecular weight of 30 kDa and instead observed a major band of approximately 72 kDa on immunoblotting with anti-PfSir2 sera (Freitas-Junior et al., 2005).

The results confirmed the existence of a substantial discrepancy between the predicted and observed lateral spreads, indicating a need for improved models that consider uncertainties in the input parameters.

However, there was a discrepancy between the predicted transcript and the sequences of EST clones CK032208 and CN694284.

Then, it discusses the results of a detailed thermal comfort analysis performed to find the reasons for the discrepancy between the predicted and observed values and the factors responsible for such discrepancy.

We chose to use the CC to evaluate the trained models because it measures whether the 'shape' of the model predictions matches the 'shape' of the 'correct' transcriptional output, and compared with the RMS error metric, CC is less sensitive to discrepancies in the magnitude of the predicted output.

The prediction models constructed with regression modeling were not useful in clinical practice, because of considerable discrepancies between the predicted and the actual peak pressure values.