Computation of Entropy Production in Stratified Flames Based on Chemistry Tabulation and an Eulerian Transported Probability Density Function Approach

This contribution presents a straightforward strategy to investigate the entropy production in stratified premixed flames. The modeling approach is grounded on a chemistry tabulation strategy, large eddy simulation, and the Eulerian stochastic field method. This enables a combination of a detailed representation of the chemistry with an advanced model for the turbulence chemistry interaction, which is crucial to compute the various sources of exergy losses in combustion systems. First, using detailed reaction kinetic reference simulations in a simplified laminar stratified premixed flame, it is demonstrated that the tabulated chemistry is a suitable approach to compute the various sources of irreversibilities. Thereafter, the effects of the operating conditions on the entropy production are investigated. For this purpose, two operating conditions of the Darmstadt stratified burner with varying levels of shear have been considered. The investigations reveal that the contribution to the entropy production through mixing emerging from the chemical reaction is much larger than the one caused by the stratification. Moreover, it is shown that a stronger shear, realized through a larger Reynolds number, yields higher entropy production through heat, mixing and viscous dissipation and reduces the share by chemical reaction to the total entropy generated.     

Optimization of Ultrasonic-Assisted Extraction of Active Components and Antioxidant Activity from Polygala tenuifolia: A Comparative Study of the Response Surface Methodology and Least Squares Support Vector Machine

Dried roots of Polygala tenuifolia (YuanZhi in Chinese) are widely used in Chinese herbal medicine. These components in YuanZhi have significant anti-oxidation properties owing to high levels of 3,6’-disinapoylsucrose (DISS) and Polygalaxanthone III (PolyIII). In order to efficiently extract natural medicines, response surface methodology (RSM) and least squares support vector machine (LSSVM) were used for the modeling and optimization of ultrasound-assisted extraction of DISS and PolyIII together to determine the antioxidant activity of the extracts obtained from YuanZhi. For the optimal combination of the comprehensive yield of DISS and PolyIII (Y), the Box-Behnken design (BBD) was used to improve extraction time (X₁), extraction temperature (X₂), liquid–solid ratio (X₃), and ethanol concentration (X₄). The optimal process parameters were determined to be as follows: extraction time, 93 min; liquid–solid ratio, 40 mL/g; extraction temperature, 48 °C; and ethanol concentration, 67%. With these conditions, the predictive optimal combination comprehensive evaluation value is 13.0217. It was clear that the LS-SVM model had higher accuracy in predictive and optimization capabilities, with higher antioxidant activity and lower relative deviations values, than did RSM. Hence, the LS-SVM model proved to be more effective for the analysis and improvement of the extraction process.     

交叉分段差分进化支持向量回归的气体超声 流量计测量方法

为了进一步提高全量程气体超声流量计的测量精度,基于多通道声波到时和实时温度,提出了一种交叉分段差分进化(Differential Evolution)支持向量回归(Support Vector Regression)DE-SVR模型。考虑到气体在不同流量条件下的流体状态不同,提出了交叉分段处理的方法,采用DE算法优化选取SVR参数。实验结果表明,对于16~1600m3/h全量程,交叉分段DE-SVR和传统积分方法计算气体流量的平均相对误差分别为0.00447和0.02781,前者较后者降低了83.93%;对于16~160m3/h小流量,交叉分段DE-SVR和无分段DE-SVR算法计算结果平均相对误差分别为0.00436和0.03214,前者较后者降低了86.43%。该方法有效避免了声道长度、探头角度以及管道直径等参数不确定性对流量计算的影响,为全量程气体流量的高精度测量提供了保障。     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

变系数计量经济学联立模型的局部线性工具向量估计及其性质

联立方程计量经济学模型在经济政策制定、经济结构分析和经济预测方面起着重要作用。本文首次提出了我国宏观经济的一类变系数联立模型,并建立了函数系数的局部线性工具向量估计,同时在时间点列固定设计、经济变量随机设计条件下,研究了估计量的大样本性质。与我国宏观经济经典线性联立模型相比,变系数联立模型拟合效果更优。另外它也有助于克服我国宏观经济数据不多而造成的非参数方法应用困难的现实情况。     

伪辛空间的生成及其表现形式

通过对辛空间的深入分析和研究,从空间中的度量与向量的迷向性问题入手并展开较为详细的讨论,定义"伪辛"的概念,阐述"辛"概念的演变、扩充与伪辛空间生成的事实,进而揭示伪辛空间的本质属性及其与辛空间的内在联系与区别.论述过程同时给出了伪辛空间关于辛空间的内蕴和外延表示式.     

自组装共混制备PEG化基因载体

通过含PEG链段的两亲聚合物的自组装共混, 制备了基于疏水作用力的新型PEG化非病毒基因载体. 分别选用胆固醇-聚乙二醇和聚乙二醇-聚丙二醇-聚乙二醇作为共混改性剂, 研究两亲聚合物的种类对组装体在生理盐溶液中的稳定性及基因转染效率的影响. 结果表明, 疏水驱动力的大小是获得稳定的PEG化基因超分子组装体的关键. 通过对两亲聚合物中疏水链段的选择调控, 可制备稳定的PEG化基因超分子组装体, 提高基因传递体系在生理盐溶液中的稳定性及基因转染效率. 通过自组装共混, 为新型PEG化基因超分子组装体的制备提供了切实可行的新方法.     

支持向量回归同时测定苯丙氨酸、酪氨酸和色氨酸的研究

在波长200~400 nm范围内,测定酪氨酸、色氨酸和苯丙氨酸混合体系的吸光度,用连续小波变换(CWT)对光谱数据进行预处理,再用支持向量回归(SVR)方法进行建模,建立了支持向量回归紫外分光光度法同时测定酪氨酸、色氨酸和苯丙氨酸的方法,用所建方法对模拟样品进行了测定。结果表明,酪氨酸、色氨酸和苯丙氨酸预测结果的回收率在98%~102%之间,测定结果准确。     

离散小波变换-支持向量回归方法同时测定苯酚、苯胺和苯甲酸

在波长范围200～400nm测定苯酚、苯胺和苯甲酸混合液的吸收光谱,用离散小波变换（DWT）对光谱数据进行处理,再用支持向量回归SVR方法进行建模,建立了离散小波变换一支持向量回归方法（DWT—SVR）。方法用于模拟样品和污染水样中苯酚、苯胺和苯甲酸的同时测定,结果满意。     

互感系数M12=M21的证明

从定义、磁能、磁矢势、磁偶极子、自由能5个方面出发分别证明了互感系数M12=M21,并对几种证明方法进行了分析对比,提出了自己的看法.