Search

Loxoprofen sodium (LOX) is a propionic acid derivative non-steroidal anti-inflammatory drug (NSAID), existing in the form of four stereoisomers. The main metabolites of LOX in vivo were trans - and cis -alcohol, each consisting of four stereoisomers. The objectives of the present study were to examine the selective pharmacokinetic behavior of LOX stereoisomers and stereoselective formation of its alcohol metabolites in rats based on a chiral liquid chromatographytandem mass spectrometry (LC-MS/MS) method by using an FLM Chiral NQ-RH column, which was reported for the first time. The significant difference in pharmacokinetic parameters of four stereoisomers indicated that stereoselective behavior has occurred in rats after oral administration of LOX. (1′ S ,2 S)-LOX showed the highest concentration among the four stereoisomers in both plasma and urine samples. Trans - and cis -alcohol metabolites of LOX were also detected in plasma and urine, and trans -alcohol was found to be primary and the stereoisomeric fractions (SF s) of its four stereoisomers at different times were calculated. Examination of the stereoisomeric composition indicated a stereo preference for (2 S)-configuration with respect to trans -alcohol formation. The overall results of the present study revealed the enantioselective pharmacokinetic properties of LOX stereoisomers in rats, which provided a means to advance understanding of the complex pharmacokinetic of LOX. [ABSTRACT FROM AUTHOR]

Coal resources play a strategic role in long-term development in China. However, underground mining activities in high groundwater table areas result in dramatic land use transition, putting both the ecosystem and the environment under great pressure. This paper examines the dynamic patterns of land use in high groundwater coal basins (HGCBs) using high-resolution Landsat TM (Thematic Mapper) data from 1985, 1995, 2005, 2010, and 2015, and socio-economic data from research institutes and government departments, and assesses the changes of ecosystem service value (ESV) by drawing a connection between the observed land use dynamics and the evaluation of ESV, based on the latest research of Xie et al. (2015) and some revisions adapted to the situation in the mining areas. The outcomes indicated that from 1985 to 2005, the ESV of the study area had a sustained growth, from 308.66 million RMB¥ in 1985–481.50 million RMB¥ in 2005. However, this growth has since stopped and turned into a decrease, with ESV dropping to 334.27 million RMB¥ in 2015. The reasons for the changes in ESV were analyzed: the large transition of construction land and farmland to waterbody caused by mining subsidence and restoration activities play a positive role, while the urbanization process in mining areas leading to the expansion of cities, and drought for the past 5 years leading to the shrinkage of waterbody, bring negative efforts to ESV. Based on the analysis above, some of the major implications were discussed. It is argued that the mining-induced land use transition will continue in the near future in HGCBs. Therefore, in order to fully consider the future landscape and thus optimize land use and extend urban development space it is necessary: to make pre-subsidence plans to maximize the farmland and ensure food security; to restore the secondary waterbody; to protect regional ecosystem service function and maintain its stability; and to make urban planning on the basis of subsidence prediction at every stage. Only in this way can HGCBs realize the sustainable use of ecological resources, as well as the integration of economic, social and ecologic benefits. [ABSTRACT FROM AUTHOR]

In this article, zinc-borosilicate silicon（ZBS） glass containing europium (Eu) ions and CsPbBr 3 quantum dots (QDs) (ZEQ) was synthesized via melting crystallization route. The composition and luminescent properties are investigated by X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) and photoluminescence (PL) spectra. The XRD and XPS results imply that Eu 3+, Eu 2+ ions and CsPbBr 3 QDs are coexisted in ZBS glass. The dual-mode emission from cyan to orange-red can be achieved by adjusting the melting temperature. Besides, the ZEQ has good water resistance, outstanding light irradiation and excellent heating-cooling cycle stability. Moreover, based on the ZEQ, the light-emitting diodes (LEDs) are designed to obtain the white light with the correlated color temperature (CCT) of 5255 K, the Coherent Infrared Energy (CIE) color coordinate of (0.337 0.313), the NSCT color gamut of 116 % and 87 % of Rec.2020. Thus, this article provides a novel material ZEQ, which is tunable luminescence and ultra-stable. The strategy provides the route that the luminescence of Eu ions and QDs can be observed at the same time, solving the difficulty of the rare earth ions entering into the QDs lattice. Moreover, the ZEQ is expected to be used as the light source for solid-state lighting and also provides valuable research results for future exploration of functional glass containing both rare-earth ions and QDs materials. [ABSTRACT FROM AUTHOR]

• A remote sensing inversion method for saline and alkaline land based on D-TELM. • The advantages of this method are high accuracy. • The different hidden layers of D-TELM have different activation functions. In order to obtain the distribution of saline and alkaline land in Wuyuan County and take appropriate improvement measures, it is necessary to invert the distribution map accurately. This paper introduces an analysis method for saline and alkaline land, which is used to analyze the salt content (‰), organic matter content (g/kg), and PH value. First of all, thirteen saline and alkaline land samples of Wuyuan County were collected in the field. Then, the spectral data was acquired by the SVC HR-1204 portable spectrometer, and the salt content, organic matter content, and PH value data were acquired by chemical analysis. Taking different hidden layers with different activation functions as the innovation point, the TELM is improved to propose D-TELM algorithm. Finally, D-TELM was used to construct three model of the relationship between the spectral data and the salt content, organic matter content, and PH value data, and the inversion and distribution maps were performed. The experimental result shows that most of the D-TELM models are better than the TELM models. According to the principle of minimum standard deviation, the inversion model of salt content uses the dsig-sin model, the inversion model of organic matter content uses the tanh-dsig model, and the inversion model of pH value uses the dsig-sin model. [ABSTRACT FROM AUTHOR]

The photoelectric properties of lead halide perovskite have been extensively investigated. However, its application in the field is greatly limited due to its poor instability and high lead content. With the aim of addressing these problems, in this study, Cs 2 ZnBr 4 :Pb2+/Mn2+ microcrystals were initially synthesized using a simple precipitation method. By manipulating the lead concentration, the emission ranging from cyan to green could be adjusted in Cs 2 ZnBr 4 :Pb2+. Co-doped Cs 2 ZnBr 4 :Pb2+/Mn2+ microcrystals with the precise regulation of the doping concentration of Pb2+ and Mn2+ were obtained through precipitation. The dual-mode emission of cyan and orange-red is successfully obtained. It is important that single white light emission is achieved in Cs 2 ZnBr 4 :Pb2+/Mn2+ microcrystals, exhibiting tunability from cold white to warm white. Moreover, Cs 2 ZnBr 4 :Pb2+/Mn2+ microcrystals are demonstrated remarkable air stability: it retains 86.27 % of the initial fluorescence intensity after 45 days in ambient conditions; it exhibits excellent UV stability, with 88.3 % of the initial fluorescence intensity maintained even after nearly 30 h of ultraviolet irradiation. Therefore, this research is anticipated to have broad applications not only in the field of solid-state lighting as UV-excited white LEDs but also in serving as a valuable reference for the exploration of low-lead halide luminescent materials. [ABSTRACT FROM AUTHOR]

• Pretreatment combined algorithms combining multiple mathematical transformations and dimensionality reduction algorithms to comprehensively process raw spectral data. • The advantage of this method is high accuracy". • A method for selecting the optimal Pretreatment combined algorithm is proposed and can accurately predict the accuracy of skarn-type iron ore. Iron ore grade determination is a key step in mining production, and its efficiency has a significant impact on mining efficiency and profitability. At present, the most commonly used method for ore grade determination is chemical analysis, which not only is expensive but also has a long assay cycle and lag effect relative to the ore allocation process and cannot effectively reduce the loss depletion rate of ore mining. Therefore, in situ iron ore grade data determination technology based on visible and near-infrared hyperspectral data analysis is an effective way to solve the above problems, but the poor quality of raw hyperspectral data and low accuracy of the ore grade inversion model are still one of the bottlenecks in the application of this technology. In this paper, 225 ore samples were collected from Hongling skarn-type iron ore in Chifeng, Inner Mongolia Autonomous Region, China. The visible and near-infrared hyperspectral data of the sample set and the grade data obtained from chemical analysis were used as data sources. The raw spectral data were first smoothed, and then the smoothed spectral data were processed using two algorithms, continuum removal (CR) and standard normal variate transform (SNV). Then, two dimensionality reduction algorithms, principal component analysis (PCA) and genetic algorithm (GA), were used to reduce the dimensionality of the spectral data before and after preprocessing, and the data sources were obtained after the four different preprocessing combination algorithms were processed. Finally, several quantitative inversion models were established based on the random forest algorithm and the backpropagation neural network (BPNN) algorithm. The stability, accuracy and credibility of the models were evaluated by four metrics: the coefficient of determination (R2), root mean square error (RMSE), residual prediction deviation(RPD) and mean relative error (MRE). The results showed that the quantitative inversion model (SNV-PCA-BPNN) established based on the BP neural network algorithm using the data processed by SNV and PCA for dimensionality reduction was the best, with an R2 of 0.99, an RMSE of 0.0048, an MRE of 1.10% and a RPD of 37.70, providing an effective method for the high-precision inversion of iron ore grades. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for rapid and accurate detection of soil organic matter content. • The method is based on reflectance spectroscopy and machine learning. • Quality band combinations are obtained by spectral pre-processing methods. • Genetic strategy is introduced into the model to enhance its exploration capability. • We propose an improved hybrid extreme learning machine model. The continuous exploitation of mining resources and a number of other factors have led to serious soil pollution problems. Soil organic matter (SOM) can be used as an important measure of soil fertility and ecological environment quality in mining areas. The traditional method of measuring SOM content by chemical methods is costly and time-consuming, so it is important to develop a rapid, accurate and economical method to measure SOM content in reclaimed mines for the ecological restoration and the evaluation of the ecological reclamation effect of mines. In this paper, the visible-near infrared spectral data of soil were obtained using an open pit mine as the study area. The three pre-processing methods of multiple scattering correction, continuous removal and Savitzky-Golay smoothing were compared. Then the two-band ratio spectral index RI was constructed to obtain the sensitive band combinations. To address the problem that the HHO-TELM hybrid model tended to converge prematurely, the GHO-TELM model was proposed in this paper. GHO-TELM introduced a gene learning strategy in HHO-TELM. The crossover, mutation and selection mechanisms were used to cultivate high-quality examples, enhance the quality of individuals and prevent premature convergence of the model. GHO-TELM was compared with several machine learning models. The experimental results showed that GHO-TELM obtained the best performance indexes, with correlation coefficient (r) of 0.9586, and relative percentage deviation (RPD) of 3.0823, indicating that the model had a good prediction effect. [ABSTRACT FROM AUTHOR]

• The spectral data are pre-processed using three dimensionality reduction algorithms. • Optimize the parameters of the dimensionality reduction algorithm. • Establishment of a high-precision grade inversion model for Derni copper deposit. At present, chemical analysis is the main method for copper ore grade verification. Because of its long test cycle and lag effect relative to mining and ore blending processes, this approach cannot effectively reduce losses in mineral resource mining. An effective way to solve this problem is to conduct research on in situ grade determination methods for ore bodies based on visible-near-infrared hyperspectral grade modelling techniques. However, the accuracy of the modelling is seriously reduced by the large redundancy of hyperspectral data and the self-limiting nature of some machine learning methods. Therefore, the accurate selection of dimensionality reduction algorithms and machine learning algorithms determines the accuracy of the inverse model, and the setting of parameters such as dimensionality reduction, number of nearest neighbour points and number of hidden layers in neural networks is crucial. In this paper, 190 ore samples were collected from the open pit of the Derni copper deposit in Dawu town, Guoluo Tibetan Autonomous Prefecture, Qinghai Province, China. First, the visible-near-infrared spectral data for the sample set were obtained with an SVC HR-1024 spectrometer. The copper grade of the sample set was verified by chemical analysis. Three algorithms—the Laplacian eigenmap (LE), local tangent space alignment (LTSA) and local linear embedding (LLE)—were used to reduce the dimensionality of the original hyperspectral data. The original data and the three hyperspectral datasets after dimensionality reduction were used as data sources. Several Derni copper grade inversion models based on two machine learning algorithms, the backpropagation neural network and radial basis function (RBF) neural network, were developed. Finally, the coefficient of determination (R2), mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate the accuracy of each model. The results show that the accuracy of the model built with spectral data processed by the dimension reduction algorithm is higher than that of the model constructed with the original spectral data. Among the above combined models, the LLE-RBF model has the highest inversion accuracy. The MAE of the model is 0.117%, the RMSE is 0.136, and R2 is 0.934. [ABSTRACT FROM AUTHOR]

• Combination of multiple pre-processing methods to comprehensively process raw spectral data. • The advantages of this method are high accuracy. • A method for selecting the optimal principle of spectral index is proposed. With saline-alkali land in Zhenlai County, Baicheng city, Jilin Province, China as the research object, a quantitative inversion model for the heavy metal content of manganese (Mn), cobalt (Co) and iron (Fe) in saline-alkali soil and the visible-near-infrared spectroscopy data were established. First, Savitzky-Golay (SG) smoothing, multivariate scattering correction (MSC), continuum removal (CR), and combined transformation methods were performed on the original spectral data. By analysing the correlation between processed data and heavy metal content, the characteristic bands corresponding to different spectral transformations were extracted. Next, the ratio index (RI), difference index (DI) and normalized difference index (NDI) were constructed, and the spectral index was determined to have the most significant correlation with the combination of Mn-, Co- and Fe-content-sensitive bands and its corresponding Spearman rank correlation coefficient. Finally, a quantitative inversion model for the heavy metal content (Mn, Co and Fe) in soda saline-alkali land was established, and its accuracy was verified. The research results show that the optimal band selection principle for the quantitative inversion model of heavy metals in soda saline-alkali land is as follows: Mn selected 219 groups of sensitive bands with correlation coefficient r > 0.70, Co selected 1377 groups of sensitive bands with correlation coefficient r > 0.80, and Fe selected 104 groups of sensitive bands with correlation coefficient r > 0.80. Based on these selection principles, the random forest algorithm was used to perform an inversion for the Mn, Co and Fe contents with the best results, the goodness of fit (R2) values between predicted and measured values were 0.76, 0.92 and 0.91, and the mean relative accuracy was 91.75%, 92.45% and 93.90%, respectively. This method has improved prediction accuracy of the Mn, Co and Fe contents in saline-alkali land. [ABSTRACT FROM AUTHOR]