[Physical fingerprint for quality control of Reduning injection].

The method of physical fingerprint spectrum for Reduning injection (RI) was proposed in this paper to improve its quality standards based on the strong correlation between physicochemical properties of drugs, their safety, effectiveness and stability. The quality of RI was studied by the thought and method of physical chemistry. The physical fingerprint spectrum was visually showed by the radar map, and consisted of eight indexes (pH, conductivity, turbidity, refractive index, osmolarity, surface tension, relative density, and kinematic viscosity). Then 12 batch of samples were verified. It was found that the physical fingerprint spectra of 3 batches of RI were in line with the standards within their validity time, with similarity above 0.999; in addition for the expired 9 batches of RI, their physical fingerprint spectra did not meet the standards. The results showed that physical fingerprint spectrum can be used for the quality control of RI, with a certain exemplary role in the quality evaluation of traditional Chinese medicine injection.

Predictive modeling of human operator cognitive state via sparse and robust support vector machines.

The accurate prediction of the temporal variations in human operator cognitive state (HCS) is of great practical importance in many real-world safety-critical situations. However, since the relationship between the HCS and electrophysiological responses of the operator is basically unknown, complicated and uncertain, only data-based modeling method can be employed. This paper is aimed at constructing a data-driven computationally intelligent model, based on multiple psychophysiological and performance measures, to accurately estimate the HCS in the context of a safety-critical human-machine system. The advanced least squares support vector machines (LS-SVM), whose parameters are optimized by grid search and cross-validation techniques, are adopted for the purpose of predictive modeling of the HCS. The sparse and weighted LS-SVM (WLS-SVM) were proposed by Suykens et al. to overcome the deficiency of the standard LS-SVM in lacking sparseness and robustness. This paper adopted those two improved LS-SVM algorithms to model the HCS based solely on a set of physiological and operator performance data. The results showed that the sparse LS-SVM can obtain HCS models with sparseness with almost no loss of modeling accuracy, while the WLS-SVM leads to models which are robust in case of noisy training data. Both intelligent system modeling approaches are shown to be capable of capturing the temporal fluctuation trends of the HCS because of their superior generalization performance.