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Knowledge that can be obtained as a result of theoretical predictions can significantly affect the process of designing new, safer ionic liquids, with properties more accurately corresponding to users needs.
Errors in ortholog predictions can significantly affect downstream analyses; as a result there has been increasing interest in high quality ortholog prediction techniques.
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Several case studies highlighting specific weather and climate-sensitive aspects of the Canadian energy sector are then used to illustrate some of the non-environmental factors governing how the economic value of a prediction can significantly vary.
From these simulation results, the MMSE detector with the proposed time-domain RML-based channel-tracking algorithm and the one-step channel prediction can significantly improve the performance of MC-CDMA under the fast fading channel and colored noise.
This is important since, when the number of sequences is relatively small, such "self-prediction" can significantly affect the results, especially in the dinucleotide case.
Analysis of data from 24 patients demonstrates that incorporation of suitable real-time signal processing algorithms for denoising, enhancement, and prediction can significantly improve the performance of CGM applications.
Overall, the single top-ranked ('top 1') predictions are important for the utility of the method, as the aim is to find reliable predictions that can significantly reduce the set of required validation experiments.
The acceleration predictions from nonlinear programs are reasonably close for most cases, but the peak strain predictions can be significantly different despite using identical backbone curves.
The overall agreement between the measured and the computed variance is satisfactory and the extended CMC model is applied to Sandia Flames D, E, and F. Excellent predictions of temperature, major species, intermediates, and NO are obtained in Flames D and E while temperature predictions can be significantly improved in Sandia Flame F.
Network features were found to be most important for accurate prediction and can significantly improve the prediction performance.
In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance.
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