Effectiveness of Traditional Chinese Medicine Compound JieDuTongLuoShengJin Granules Treatment in Primary Sjögren’s Syndrome: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial

Objective. To evaluate the clinical therapeutic efficacy and safety of JieDuTongLuoShengJin granules + HCQ in patients with pSS. Methods. 40 patients with low-activity-level pSS and without visceral involvement participated in this study and were randomized to receive either JieDuTongLuoShengJin granules with HCQ or placebo with HCQ. Patients and investigators were blinded to treatment allocation. The primary endpoint was week 12 ESSPRI score, while secondary endpoints included ESSDAI, salivary and lacrimal gland function, and some laboratory variables. Safety-related data were also assessed. Results. Comparing with the placebo group, the treatment group experienced statistically significant improvement in the mean change from baseline for the primary endpoint of ESSPRI score and also in PGA. Moreover, in comparison with baseline values, the treatment group had significantly improved ESSDAI score, unstimulated saliva flow rate, and several laboratory variables. However, upon comparison of the two groups, there were no significant differences for them. The incidence of AEs was 10.0%, one in treatment group and three in placebo group. Conclusion. Treatment with a combination of JieDuTongLuoShengJin granules with HCQ is effective in improving patients’ subjective symptoms and some objective indicators of pSS. These results indicate that JieDuTongLuoShengJin is promising as a safe and effective treatment of pSS

Enhancement of nonlinear optical properties of composite material based on Al

We fabricate the Fe:Al2O3 composite material by using the ion implantation technique. The implantation was performed at energy of 35 KeV with ion concentration of $1.2\times 10^{17}\ \text{ions/cm}^{2}$ . The supercontinuum spectrum was measured to show the nonlinear refractive property of this kind of sample. The Kerr-lens autocorrelation method was used to measure the nonlinear optical refractive index of this Fe:Al2O3 composite material for the first time. The measuring result was $7.7\times 10^{-16}\ \text{cm}^{2}/\text{W}$ at a wavelength of 800 nm. The nonlinear optical refraction of Al2O3 can be dramatically enhanced by Fe ion implantation. The XPS spectrum was measured to analyze the valence state of Fe in the surface of this composite material

Denoising data acquisition algorithm for array pixelated CdZnTe nuclear detector

Traditionally, the binary search method is used to collect the denoising data in the array pixilated CdZnTe nuclear detector. Due to the high dispersion of the data itself, the acquisition efficiency is low and the acquisition result has a large error. A denoising data acquisition algorithm for array pixilated CdZnTe nuclear detector is proposed. The detector principle and system noise type are analyzed. The buffer half-full storage algorithm and multi-thread control method are used to collect the noise data of array pixilated CdZnTe nuclear detector. The experimental data show that the proposed algorithm can effectively collect the denoising data of the array pixilated CdZnTe nuclear detector, and the acquisition error rate is only 0.25, the acquisition speed growth rate is up to 96%, with high acquisition accuracy and efficiency

Detection of Cause-Effect Relations Based on Information Granulation and Transfer Entropy

Causality inference is a process to infer Cause-Effect relations between variables in, typically, complex systems, and it is commonly used for root cause analysis in large-scale process industries. Transfer entropy (TE), as a non-parametric causality inference method, is an effective method to detect Cause-Effect relations in both linear and nonlinear processes. However, a major drawback of transfer entropy lies in the high computational complexity, which hinders its real application, especially in systems that have high requirements for real-time estimation. Motivated by such a problem, this study proposes an improved method for causality inference based on transfer entropy and information granulation. The calculation of transfer entropy is improved with a new framework that integrates the information granulation as a critical preceding step; moreover, a window-length determination method is proposed based on delay estimation, so as to conduct appropriate data compression using information granulation. The effectiveness of the proposed method is demonstrated by both a numerical example and an industrial case, with a two-tank simulation model. As shown by the results, the proposed method can reduce the computational complexity significantly while holding a strong capability for accurate casuality detection