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为了探索基于近红外光谱技术快速无损鉴别掺假油茶籽油的可行性，以赣南茶油为研究对象，通过掺入不同植物油如玉米油、花生油、菜籽油、葵花籽油和大豆油等制备掺假油茶籽油，应用近红外光谱技术采集其光谱特征信息，对比不同预处理方法和主成分数，并结合线性和非线性建模方法建立油茶籽油掺假鉴别模型，以识别准确率(纯油茶籽油样品和掺假油茶籽油样品被正确判别的比例）、灵敏度（纯油茶籽油样品被正确判别为纯油茶籽油的比例）、特异性（掺假油茶籽油样品被正确判别为掺假油茶籽油的比例）作为模型的评价指标，优选出最佳模型。结果表明：二阶微分联合线性判别分析(SD-LDA）模型为最优线性模型，标准正态变量变换联合人工神经网络(SNV-ANN）模型为最优非线性模型，两个模型的识别准确率、灵敏度、特异性分别为 97.58%、100%、97.33%和98.99%、100%、98.88%。SNV-ANN 模型鉴别效果优于SD-LDA模型，说明非线性模型更适于油茶籽油掺假判别，该模型能更准确地鉴别油茶籽油是否掺假。 In order to explore the feasibility of rapid and non-destructive identification of adulterated oil-tea camellia seed oil based on near infrared spectroscopy, Gannan oil-tea camellia seed oil was selected as the research object, and the adulterated oil-tea camellia seed oil was prepared by blending different vegetable oils such as corn oil, peanut oil, rapeseed oil, sunflower seed oil and soybean oil. The spectral characteristics of the adulterated oil-tea camellia seed oil were collected by near infrared spectroscopy, and different pretreatment methods and main components were compared and determined. The identification model of oil-tea camellia seed oil adulteration was established by combining linear and nonlinear modeling methods. The identification accuracy(the percentage of pure and aduterated oil-tea camellia seed oil samples correctly identified, sensitivity (the percentage of pure oil-tea camellia seed oil samples correctly identified as pure oil-tea camellia seed oil) and specificity (the percentage of adulterated oil-tea camellia seed oil samples correctly identified as adulterated camellia seed oil) were used as the evaluation indexes of the model to select the best model. The results showed that the second derivative-linear discriminant analysis (SD-LDA) model was the optimal linear model, and the standard normal variable transformation-artificial neural network (SNV-ANN) model was the optimal nonlinear model, their identification accuracy, sensitivity and specificity were 97.58%, 100%, 97.33% and 98.99%, 100%, 98.88% respectively. The identification effect of SNV-ANN model was better than that of SD-LDA, which indicated that the nonlinear model was more suitable for the identification of oil-tea camellia seed oil adulteration, and the model could more accurately identify whether the oil-tea camellia seed oil was adulterated.

Alkylphenols are a kind of compounds produced by alkylation of phenolic compounds. Compared with other non-hydrocarbon compounds, alkylphenols are more soluble in water. In this paper, through three groups of oil-water distribution experiments at different temperatures (25 ℃, 45 ℃ and 65 ℃), different brine concentrations (4000 mg/L, 6000 mg/L and 8000 mg/L) and different crude oil types (X37, X45 and X61), the distribution characteristics of alkylphenols between oil and water were studied, and the variation law of alkylphenol distribution coefficient was determined. The results show that the oil-water partition coefficient increases with the increase of alkyl molecular weight of homologues; The oil-water partition coefficient of alkylphenols decreases with the increase of temperature, and alkyl homologues with higher molecular weights are more sensitive to temperature; The partition coefficient of alkylphenol in oil-water system increases with the increase of water salinity. According to the experimental results, temperature and salinity of water phase will cause the difference of oil-water partition coefficient of alkylphenol, and the influence of temperature is greater than that of salinity. The distribution behavior of alkylphenols between oil and water can provide more reference for the secondary migration of crude oil and the direction of water drive front in reservoir development.

We present a decade of observations of pulse arrival times for 85 pulsars using the Nanshan radio telescope from July 2002 to March 2014. The Cholesky method can accurately estimate the covariance function of the timing residuals, significantly improving the parameter's estimation accuracy when red noise is prominent. We utilize the Cholesky method to determine positions and basic timing parameters of these pulsars, as well as to obtain timing residuals. Most of these sources showed evidence of significant timing irregularities, which are described. The spectral analyses of timing residuals are presented for pulsars showing obvious red noise. Our results show that timing residuals in half of these pulsars are attributed to rotational irregularities. The red noise in normal pulsars may originate from a random walk in spin frequency or spin-down rate. [ABSTRACT FROM AUTHOR]

Accurately predicting permeability is important to elucidate the fluid mobility and development potential of tight reservoirs. However, for tight sandstones with the same porosity, permeability can change by nearly three orders of magnitude, which greatly increases the difficulty of permeability prediction. In this paper, we performed casting thin section, scanning electron microscopy and high-pressure mercury injection experiments to analyze the influence of pore structure parameters and fractal dimensions on the permeability of Chang 7 tight sandstones. Furthermore, the key parameters affecting the permeability were optimized, and a new permeability prediction model was established. The results show that the pore throat structure of Chang 7 tight sandstone exhibits three-stage fractal characteristics. Thus, the pore throat structure was divided into large pore throat, medium pore throat and small pore throat. The large pore throat reflects the microfracture system, whose fractal dimension was distributed above 2.99, indicating that the heterogeneity of the large pore throat was the strongest. The medium pore throat is dominated by the conventional pore throat system, and its fractal dimension ranged from 2.378 to 2.997. Small pore throats are mainly composed of the tree-shaped pore throat system, and its fractal dimension varied from 2.652 to 2.870. The medium pore throat volume and its fractal dimension were key factors affecting the permeability of Chang 7 tight sandstones. A new permeability prediction model was established based on the medium pore throat volume and its fractal dimension. Compared to other models, the prediction results of the new model are the best according to the analysis of root mean square value, average absolute percentage error and correlation coefficient. These results indicate that the permeability of tight sandstones can be accurately predicted using mesopore throat volume and fractal dimension. [ABSTRACT FROM AUTHOR]