For
the more complex samples, chemical higher-order data can be
collected from various information sources, which become the necessary
foundation of accurate analysis. In this article, the Tchebichef cubic
moment (TCM) was developed for the analysis of chemical third-order
data for the first time. Then, the proposed TCM approach was applied
to the fluorescence excitation–emission time data for the analysis
of adrenaline and noradrenaline in urinary samples (Data I) and the
data fusion of the excitation–emission matrix (EEM), NMR, and
liquid chromatography-mass spectrometry (LC-MS) spectra for the determination
of the five target components (Data II). For Data I, all of the cross-validation
correlation coefficients (R
cv
2) of the obtained linear models on the
calibration set were more than 0.9937 and the prediction root-mean-square
errors (RMSEp) of the external independent test samples
were less than 0.0250 μM. For Data II, all of the R
cv
2 were higher
than 0.9846 and RMSEp were less than 0.2267 μM. Compared
with several conventional methods, the proposed method was more convenient
and accurate. This study provides another effective approach to the
analysis of complex samples based on their chemical third-order data.
Efficient dynamic shortest path algorithm in static networks plays an important role in ITS. To solve this problem, this paper brings forward the dynamic form of Consistency Assumption and Dynamic A* algorithm (A* algorithm based on dynamic lower bound, DA* algorithm) based on dynamic lower bound. DA* algorithm and the dynamic form of Consistency Assumption are described in detail. It is proved that DA* algorithm can solve one origin node to one destination node shortest paths problem in dynamic networks, if DA* algorithm’s dynamic lower bound satisfies the dynamic form of Consistency Assumption.
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