In a recent publication, we carried out an extensive analysis of an unsupervised method of determining optimum shift factors in time-temperature-superposition of accelerated-aging data that involves minimizing the vertical arclength to obtain the master curve. For synthetic Arrhenius data with a variety of noise distributions, the work showed that, in conjunction with bootstrap-resampling, the method can produce reliable estimates of the mean activation energy along with uncertainty quantification. The present work applies the above method to six different datasets taken from the published literature and demonstrates accurate prediction of mean activation energy from the data as-is without the need for any pre-processing or fitting. It also compares uncertainty margins computed by second-order bootstrap with that by linear regression theory and shows that the former appears to provide consistent margins in the presence of common noise types in real data, including intra-isotherm measurement-errors, sample-to-sample variations, and intrinsic deviation from perfect Arrhenius behavior.

在最近的出版物中，我们对无监督方法进行了广泛的分析，该方法确定了加速老化数据的时间-温度叠加中的最佳偏移因子，该方法涉及最小化垂直弧长以获得主曲线。对于具有各种噪声分布的合成Arrhenius数据，该工作表明，与自举重采样结合使用，该方法可以产生平均活化能的可靠估计以及不确定性量化。本工作将上述方法应用于从公开文献中获得的六个不同的数据集，并证明了按原样从数据中准确预测平均活化能，而无需任何预处理或拟合。