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为了探索紫外-可见-近红外反射光谱测定油茶籽油掺伪量的方法，按照不同掺伪比例制备了244个油茶籽油掺伪大豆油、菜籽油、花生油、玉米油的样本，以自主搭建的实验平台采集所制备样本在200~1 100 nm范围内的反射光谱。将原始光谱进行Savitzky-Golay(SG)-连续小波变换（CWT）预处理后，利用Kennard-Stone(K-S)算法以2∶ 1的比例将样本划分成校正集和预测集。采用竞争性自适应重加权算法（CARS）、连续投影算法（SPA）、自主软收缩算法（BOSS）、迭代变量子集优化算法（IVSO）进行特征波长选择，分别建立基于支持向量机（SVM）、极限学习机（ELM）、随机森林（RF）的油茶籽油掺伪量快速预测模型，同时对特征波长的特性进行了研究。结果表明：原始光谱经过 SG-CWT（L5）预处理和 BOSS 特征波长筛选后，建立的基于SVM的油茶籽油掺伪量快速预测模型能够鉴别掺伪量为1%及以上的油茶籽油，该模型在十折交叉验证和网格搜索法下得到最佳惩罚因子（c）和核函数（γ）分别为5.278 0和0.108 8，其预测决定系数（R2P）、预测均方根误差（RMSEP）、预测平均绝对误差（MAEP）分别为0.998 5、0.013 4、0.010 2。特征波长聚集程度和陡度对模型预测结果存在一定影响。综上，建立的基于反射光谱的油茶籽油掺伪量快速预测模型预测误差小，预测效果较好。In order to explore the method of UV-Vis-NIR reflection spectroscopy to identify blended oil-tea camellia seed oil(CAO), 244 samples of CAO adulterated with soybean oil, rapeseed oil, peanut oil and corn oil were prepared according to different adulteration amounts, and the reflectance spectra of the prepared samples in the range of 200-1 100 nm were collected by an experimental platform built independently. After pretreating the raw spectra with SG-continuous wavelet transform (CWT), the samples were divided into correction and prediction sets using the Kennard-Stone (K-S) algorithm in a ratio of 2∶ 1. Competitive adapative reweighting sampling(CARS)algorithm, successive projections algorithm (SPA), bootstrapping soft shrinkage(BOSS) algorithm, and iteratively variable subset optimization (IVSO) algorithm were used for characteristic wavelength selection, and rapid identification models based on support vector machine (SVM), extreme learning machine (ELM), and random forest (RF) were established for CAO adulteration amount, respectively, and the characteristics of characteristic wavelength were studied. The results showed that the SVM model established after the SG-CWT (L5) pretreating and BOSS characteristic wavelength screening could discriminate the amount of adulteration 1% and above, and the model obtained the best penalty factor c ( 5.278 0) and kernel function γ (0.108 8) under the ten-fold cross-validation and grid search method, with R2P, RMSEP and MAEP of 0.998 5, 0.013 4 and 0.010 2, respectively. At the same time, the degree of aggregation and steepness of the characteristic wavelength had some influence on the model prediction results. In conclusion, the established rapid prediction model for the adulteration amount of oil-tea camellia seed oil based on reflection spectroscopy has low error and good prediction effect.

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Copyright of Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the accuracy of respiratory signal to distinguish driving fatigue, the relationship was explored between respiratory signal and driver fatigue state through simulated driving test. The concept of respiratory fatigue node was proposed, and the driver's fatigue state was distinguished based on the respiratory fatigue node. Firstly, the respiratory signals of drivers were collected by simulated driving test, and the Karolinska sleepiness scale (KSS) was used to quantify the subjective self-evaluation of their fatigue degree. Secondly, the percentage of eyelid closure over the pupil over time (PERCLOS) was used as a reference, combined with subjective self-evaluation feedback to calibrate the respiratory fatigue nodes of drivers. Finally, based on the respiratory fatigue nodes, the random tree(RT) algorithm was used to obtain the discriminant model of mild / severe respiratory fatigue change nodes. The results show that the model can identify the driver's respiratory fatigue state more timely and accurately and the accuracy of the screening conditions based on the random tree algorithm for the identification of mild respiratory fatigue change nodes is higher than that of severe respiratory fatigue change nodes. The average recognition errors of mild / severe respiratory fatigue change nodes are 3. 50 min and 3. 66 min, respectively, the prediction accuracy is 92. 09% and 92. 03% respectively. [ABSTRACT FROM AUTHOR]

To address the challenges posed by the poor sensitivity of traditional degradation indices to the onset of bearing degradation and the inability to accurately predict the remaining using life (RUL) of wind turbine bearings due to trend shifts in degradation indices, a biexponential method for predicting bearing RUL was proposed. This method was based on the combination of Teager's energy arithmetic-ratio of failures to energy (TFER) and degradation trend (DT) similarity detection. Firstly, the TFER value of the original signal was calculated to determine the bearing degradation starting point following the 4σ principle. The trend feature of the TFER value was then extracted as a degradation indicator. Subsequently, a bi-exponential degradation model was fitted using historical TFER values. Then, the best DT segment was chosen by analyzing the similarity between the latest TFER values and the fitted curve. Finally, the wind turbine bearing RUL was predicted by extrapolating the DT segment with the best similarity to the failure threshold. The experimental results show that the prediction method achieves 98% accuracy in detecting the degradation onset time point, and improves 10. 04%, 6. 29%, and 5. 22% in bearing RUL prediction accuracy compared with the original exponential model, the long-and short-term memory neural network (LSTM), and the support vector regression (SVR), respectively. This method not only improves the accuracy of predictive maintenance of wind turbine bearings, but also provides strong support for reducing operating costs and improving maintenance efficiency. [ABSTRACT FROM AUTHOR]

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Copyright of Gold (1001-1277) is the property of Gold Magazine Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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African swine fever virus(ASFV)possesses multiple strategies for escaping host immune responses, making it difficult for the virus to be cleared by the host. The cGAS-STING pathway, which mediates host innate immune response, is critical for fighting ASFV infection. Nevertheless, numerous viral proteins targeting different molecules involving in the cGAS-STING pathway inhibit type I interferon production. The deletion of partial of these ASFV genes using genome editing technology attenuated the virulence of the virus, which provides targets for the development of gene deletion vaccine. In this review, we summarized current knowledge on the viral proteins of ASFV modulating host cGAS-STING signaling pathway and elucidate the mechanism of these proteins escaping cGAS-STING signaling pathway, which may deepen the understanding of ASFV immune evasion strategy and provide theoretical basis for the research of ASFV pathogenic mechanism and vaccine development. [ABSTRACT FROM AUTHOR]

In response to the copper cyanide barren solution generated by a gold production company, a joint recovery treatment using complex precipitation and sulfide precipitation was adopted. Under the conditions of adding 3.0 g/L of copper sulfate pentahydrate and 2.0 g/L of sodium metabisulfite in the complex precipitation process, and adding 0.35 g/L of sodium sulfide nonahydrate in the sulfide precipitation process, the mass concentrations of total cyanide compounds, copper, and thiocyanate in the copper cyanide barren solution decreased from 150.84 mg/L, 121.46 mg/L, 252.65 mg/L to 0.44 mg/L, 86.17 mg/L, 1.23 mg/L, respectively. The production value of gold, silver, and copper recovery was 116.62 yuan/m³, generating an economic benefit of 55.44 yuan/m³ after deducting the cost of reagents. The research results provide a reference for the purification treatment of similar cyanide barren solutions in cyanide enterprises. [ABSTRACT FROM AUTHOR]

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