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Distinguishing similar gelatins are essential for pharmaceutical administration and therapy based on traditional Chinese medicine. Differed from the conventional studies, this study focuses on a reaction-free terahertz approach to identify gelatins made of asinine, bovine, and porcine skins. Samples in tablets form are measured by terahertz time-domain spectroscopy and the calculated optical parameters are processed by a principal component analysis algorithm. Additionally, linear support vector machines are used to classify gelatin varieties and the performance is discussed with respect to the number of principal components kept for modelling. It is founded that three principal components are optimal to identify similar gelatins in a reduced feature space and the extinction coefficient is more efficient in distinguishing three different gelatins. This study proves the feasibility of THz time-domain spectroscopy in gelatin administration in traditional Chinese medicine.

It is a necessity to determine significant food or traditional Chinese medicine (TCM) with low cost, which is more likely to achieve high accurate identification by THz-TDS. In this study, feedforward neural networks based on terahertz spectra are employed to predict the animal origin of gelatins, whose adaption to the mission is examined by parallel models built by random sample partition and initialization. It is found that the generalization performance of feedforward ANNs in original data is not satisfactory although prediction on trained samples can be accurate. A multivariate scattering correction is conducted to enhance prediction accuracy, and 20 additional models verify the effectiveness of such dispose. A special partition of total dataset is conducted based on statistics of parallel models, whose influence on ANN performance is investigated with another 20 models. The performance of the models is unsatisfactory because of notable differences in training and test sets according to principal component analysis. By comparing the distribution of the first two principal components before and after multivariate scattering correction, we found that the reciprocal of the minimum number of line segments required for error-free classification in 2-D feature space can be viewed as an index to describe linear separability of data. The rise of proposed linear separability would have a lower requirement for harsh parameter tuning of ANN models and tolerate random initialization. The difference in principal components of samples between a training set and a data set determines whether partition is acceptable or whether a model would have generality. A rapid way to estimate the performance of an ANN before sufficient tuning on a classification mission is to compare differences between groups and differences within groups. Given that a representative peak missing curve is discussed in this article, an analysis based on gelatin THz spectra may be helpful for studies on some other feature-less species.

Polypropylene (PP) is a widely used common type of polymer with excellent chemical and mechanical properties. Thermal aging is a main culprit in degrading its lifetime and effectiveness. In this article, terahertz (THz) dielectric frequency response of PP during the process of thermal aging is monitored and analyzed to understand the underlying mechanism and to qualify THz dielectric spectroscopy as a useful tool for time-stamping and benchmarking the thermal aging of polymer materials. Using an improved THz material parameter extraction algorithm for thin films, the real and imaginary parts of the dielectric permittivity of standard PP test coupons were obtained at various stages of the thermal aging process. In agreement with theoretical analysis based on a molecular-level understanding of the thermal aging dynamics, the experimental results indicated THz wave transmission loss of PP correlated quantitatively with the degree of its thermal aging.

Haixia Wang, Yuanmin Jia, Mo Yi, Yizhang Li, Ou Chen School of Nursing, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, People’s Republic of ChinaCorrespondence: Ou ChenSchool of Nursing, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, People’s Republic of ChinaTel +86 15990991181Email chenou@sdu.edu.cnBackground: Several cross-sectional studies have suggested an association between SUA and asthma. However, few studies have investigated this relationship longitudinally. Although SUA is an independent risk factor for chronic kidney disease and cardiovascular diseases, its contribution to incident asthma remains uncertain.Objective: To determine whether a high SUA was a risk factor for adult incident asthma.Methods: By using health care data from the Shandong multicenter health check-up, 76,369 participants, aged 20 to 79 years, were identified who had an SUA determination with a mean follow-up period of 3.73± 2.21 years. Multivariate modeling employed Cox proportional hazards models to verify the association between SUA and incident asthma by adjusting age, BMI, smoking habits, drinking habits, and asthma-like diseases.Results: A total of 51,389 individuals were included in the analysis. Cut-off values of SUA able to discriminate asthma status were identified by means of maximally selected rank statistics in the whole participants (≥ 376.80umol/L), women (≥ 314.45umol/L), and men (≥ 376.80umol/L). Multivariate Cox regression analyses adjusted for covariates (age, body mass index (BMI), smoking habit, drinking habit, and asthma-like disease) identified an independent association between SUA and incident asthma in the whole participants (hazard ratio (HR) 2.92, 95% confidence intervals (CI), 1.76– 4.48, P=0.00) and men (HR 3.02, 95% CI 1.70– 5.39, P=0.00), but not in women.Conclusion: The results of the current study confirmed that high SUA was an independent risk factor for incident asthma after adjustment of potential covariates and suggested that a cut-off value related to incident asthma could be identified only in men.Keywords: cut-off value, asthma, gender, serum uric acid, risk factor

As an intelligent human-wheelchair interaction ways, head gesture becomes one of the most pop research topics, and the head gesture recognition is an important aspect of the interactions. In this paper, kalman filter forecast the lips position detected by Adaboost algorithm may be appeared in the next frames first, and then detect the lips in the next frame. Compare the lips window position with a fixed point to confirm the head gesture correspondingly. Kalman filter overcome detect all the possible lips position by just use the Adaboost algorithm in every frame, greatly improve the lips detection precision and reduce the detection time, solve the wheelchair's time delay.