HPLC study on HPLC Characteristic Spectrum of Huoxiang (Herba Agastachis) Eliminating Summer-heat Soft Capsule

【摘要】 目的：建立火香消夏软胶囊的HPLC特征谱。方法：在Tech进行色谱分离伴侣C18-ST（5um的，4.6 × 250nm的）柱 由Agilent以1.0mL∙min的流速1260高性能液相色谱仪，洗脱的乙腈和0.05％磷酸中的梯度洗脱- 1个。检测波长设定为270nm。选择橙皮苷作为参考峰，以计算相对保留时间的RSD。还研究了精密度，稳定性和可重复性。这项研究包括来自5个制造商的80个不同样品。结果：建立了火香消暑软胶囊的HPLC特征谱图，在80个样品中具有11个特异的色谱峰。该方法准确，可靠，重现性好，可全面控制火香消暑软胶囊的质量。

Garnet Characteristics Associated with Jiama Porphyry-Skarn Cu Deposit 1# Skarn Orebody, Tibet, Using Thermal Infrared Spectroscopy

Field measurements of the thermal infrared (TIR) reflectance from drill hole samples proved to be an effective method to map variations in garnet species associated with hydrothermal alteration zonation of the Jiama porphyry-skarn Cu deposit 1# skarn orebody, Tibetan Plateau, China. The TIR mineral spectral information was combined with electron probe micro-analysis (EPMA) measurements to provide geological insights on effectively determining (a) garnet end components and providing a format for further research on the type and genesis of the deposit; (b) the significance of the characteristic spectrum of garnet to the variation of mineralization environment; (c) the relationship between the characteristic spectrum of garnet and Fe/Al content; (d) the garnet characteristic spectrum to the economic mineralization. The results suggest that garnet characteristics of the thermal infrared spectrum can be used as an indicator for skarn deposit prospecting.

Possible Wormhole Candidates in Active Galactic Nuclei

The hypothesis is considered that the active galactic nuclei (AGNs) are wormhole (WH) mouths rather than supermassive black holes (SMBHs). We study the difference in the physical properties of such objects from those of AGNs with SMBH, as well as the the corresponding difference in observational data. Firstly, the radiative efficiency for some types of WHs (both static and rotating) can be significantly larger than the theoretical maximal value for the Kerr SMBHs. A number of AGNs is presented, for which the observational data can be interpreted as the result of the presence of WHs in them. Secondly, a sufficiently strong gamma radiation with a characteristic spectrum noticeably differing from that of AGNs jets, can be emitted from a static WH as a result of a collision of accreting flows of matter inside the WH.

Dynamic Inversion of Inland Aquaculture Water Quality Based on UAVs-WSN Spectral Analysis

The inland aquaculture environment is an artificial ecosystem, where the water quality is a key factor which is closely related to the economic benefits of inland aquaculture and the quality of aquatic products. Compared with marine aquaculture, inland aquaculture is normally smaller and susceptible to pollution, with poor self-purification capacity. Considering its low cost and large-scale monitoring ability, many researches have developed spectrum sensor on-board satellite platforms to allow remote monitoring of inland water surface. However, there remain many problems, such as low image resolution, poor flexible data acquisition, and anti-interference. Apart from that, the conventional forecasting model is of weak generalization ability and low accuracy. In our study, we combine unmanned aerial vehicles system (UAVs) with the wireless sensor network (WSN) to design a new ground water quality parameter and drone spectrum information acquisition approach, and to propose a novel dynamic network surgery-deep neural networks (DNS-DNNs) model based on multi-source feature fusion to forecast the distribution of dissolved oxygen (DO) and turbidity (TUB) in inland aquaculture areas. The result of using fused features, including characteristic spectrum, Gray-level co-occurrence matrix (GLCM) texture feature, and convolutional neural network (CNN) texture feature to build a model is that the characteristic spectrum+ CNN texture fusion features were the best input items for DNS-DNNs when forecasting DO, with the determination coefficient R 2 of the vertical set arriving at 0.8741, while the characteristic spectrum+ GLCM texture+ CNN texture fusion features were the best for TUB, with the R 2 reaching 0.8531. Compared with a variety of conventional models, our model had a better performance in the inversion of DO and TUB, and there was a strong correlation between predicted and real values: R 2 reached 0.8042 and 0.8346, whereas the root mean square error (RMSE) were only 0.1907 and 0.1794, separately. Our study provides a new insight about using remote sensing to rapidly monitor water quality in inland aquaculture regions.