The spatiotemporal prediction method of urban population density distribution through behaviour environment interaction agent model.

Based on the interrelationship between the built environment and spatial-temporal distribution of population density, this paper proposes a method to predict the spatial-temporal distribution of urban population density using the depth residual network model (ResNet) of neural network. This study used the time-sharing data of mobile phone users provided by the China Mobile Communications Corporation to predict the time-space sequence of the steady-state distribution of population density. Firstly, 40 prediction databases were constructed according to the characteristics of built environment and the spatial-temporal distribution of population density. Thereafter, the depth residual model ResNet was used as the basic framework to construct the behaviour-environment agent model (BEM) for model training and prediction. Finally, the average percentage error index was used to evaluate the prediction results. The results revealed that the accuracy rate of prediction results reached 76.92% in the central urban area of the verification case. The proposed method can be applied to prevent urban public safety incidents and alleviate pandemics. Moreover, this method can be practically applied to enable the construction of a "smart city" for improving the efficient allocation of urban resources and traffic mobility.

Chromatographic Fingerprinting Based on Column Switching Technology for Quality Evaluation of Tianmeng Oral Liquid.

Separation power was limited when the conventional high-performance liquid chromatography (HPLC) fingerprinting method based on a single column was used to analyze very complex traditional Chinese medicine (TCM) preparations. In this research, a novel HPLC fingerprinting method based on column switching technology by using a single pump was established for evaluating the quality of Tianmeng oral liquid (TMOL). Twelve batches of TMOL samples were used for constructing HPLC fingerprints. Compared with the 16 common peaks in fingerprinting with a single column, 25 common peaks were achieved with two columns connected through a six-way valve. The similarity analysis combined with bootstrap method was applied to determine the similarity threshold, which was 0.992 to distinguish expired samples and unexpired samples. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) were also applied to classify the TMOL samples, and results revealed that expired and unexpired samples are classified into two categories. The HPLC fingerprinting based on column switching technology with better separation power and higher peak capacity could characterize chemical composition information more comprehensively, providing an effective and alternative method to control and evaluate the quality of TMOL, which would offer a valuable reference for other TCM preparations.

[Comparison of discriminative methods of hypoglycemic drugs by Raman spectroscopy].

In the present paper, four kinds of cluster analysis methods were used in rapid, non-destructive discrimination of hypoglycemic tablets by the Raman spectroscopy technology. Nine kinds of hypoglycemic tablets, including 48 samples, were determined using a Raman spectrometer. The sample data were pretreated with the methods of frequency range cutting, baseline correction, smoothing and vector normalization, then were analyzed by K-means, hierachical cluster, self-organizing maps (SOM) and PCA-SOM respectively. The results demonstrated that SOM was better than K-means and hierachical cluster, and it provided the best discrimination when combined with PCA. The research offers a new approach to the rapid discrimination of different kinds of hypoglycemic tablets.

[Rapid determination of hypoglycemic tablets by handheld Raman spectrometer and KPCA-clustering analysis].

In the present paper, five different kinds of hypoglycemic tablets were identified using kernel principal component analysis (KPCA)-clustering analysis of their Raman spectra. KPCA was used to compress thousands of spectral data into several variables and to describe the body of the spectra before clustering analysis was chosen as further research method. The results showed that hypoglycemic tablets could be quickly classified using KPCA-clustering analysis. A disadvantage of Raman spectroscopy for this type of analysis is that it is primarily a surface technique. As a consequence, the spectra of the tablet core and its coating might differ. However, the KPCA-clustering analysis turned out to be a sufficiently reliable discrimination, i. e., 96% of the hypoglycemic tablets with coating and 100% of the hypoglycemic tablets without coating were predicted correctly. Overall, the Raman spectroscopic method in the present paper plays a good role in the identification and offers a new approach to the rapid discrimination of different kinds of hypoglycemic tablets.