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.     

中空石墨碳材料作为电催化剂载体在直接乙醇燃料电池中的应用

以间苯二酚、苯酚与甲醛为前体,合成了一种中空石墨碳材料(hollow graphitic carbon,HGC).透射电镜(TEM)、N2吸附-脱附和Raman光谱测试结果表明,所制备的HGC为中孔结构,平均孔径为36nm.与商品Vulcan XC-72R相比,HGC中孔结构丰富,石墨化程度高.分别以HGC和XC-72R为载体制备了总金属载量为45%的PtSn/C电催化剂,X射线衍射和TEM结果表明这两个样品的平均粒径和晶格常数相近.单池性能测试表明,以45%PtSn/HGC为阳极催化剂的直接乙醇燃料电池的最大功率密度达到了62mW/cm2,与PtSn/XC-72R的54mW/cm2相比提高了近15%.     

金催化乙炔氢氯化的密度泛函理论研究进展

乙炔氢氯化(AH)是生产氯乙烯的主要途径之一,传统上使用高毒性的汞催化剂,因此开发无汞催化剂迫在眉睫。金(Au)催化剂是最有潜力的替代催化剂之一,然而其活性Au物种、反应物的活化过程或反应过渡态结构等催化机理仍不够清晰。密度泛函理论（DFT）在研究由Au催化AH的反应机理中发挥了极其重要的作用。本文综述了DFT对金催化剂活性位点、反应物在催化剂上的吸附性质及反应机理的研究进展。重点讨论了DFT对阳离子金和金簇催化AH反应过程的模拟计算,包括Au电子状态、其它原子掺杂及金簇尺寸和形状对催化AH反应影响的模拟。结果表明DFT模拟计算在微观分子尺度上研究反应物的吸附、反应中间体及过渡态等方面发挥了关键作用,对理解Au催化AH反应机理做出了重要贡献。     

基于改进NAS-RIF的三维显微图像盲复原

对二维NAS-RIF算法进行了改进,并将它用于三维显微图像复原,分别用模拟样本和真实生物样本进行了实验。针对原算法对噪声有放大作用,提出了基于中值滤波去噪的改进。在支持域的确定、最小点的搜索上进行改进,提高复原质量。改进了原算法仅适用于均匀背景的不足,扩大了应用范围。实验结果表明,该算法在三维显微图像盲复原中具有较好的效果。     

PSO-LSSVM对LIBS定量分析精度的提高

针对土壤定量分析受基体效应影响大,LIBS定量分析精度不佳等问题,采用粒子群算法对LSSVM进行优化,提高模型的精确度。选取Pb Ⅰ 405.78 nm和Cr Ⅰ 425.44 nm作为分析谱线进行分析。采集十二个不同浓度样品的特征光谱,每个浓度样品在不同点采集20组数据,将其中17组数据设为训练集,3组数据设为预测集,用LSSVM和PSO-LSSVM两种方法建立定标模型。对比两种模型的拟合相关系数（R2）、训练集均方根误差（RMSEC）和预测集均方根误差(RMSEP)。由于自吸收效应的影响,随着浓度的增加,预测值逐渐低于实际值,LSSVM定标模型的拟合程度较低,无法达到实验要求,模型性能有待提高。利用粒子群算法对LSSVM的模型参数惩罚系数和核函数参数进行优化,得到最佳的参数组合,Pb元素为(8 096.8, 138.865 7),Cr元素为(4 908.6, 393.563 5),用最佳的参数组合构建LSSVM的定标模型。相比于LSSVM,PSO-LSSVM定标模型的精确度更高,Pb和Cr元素的R2提高到了0.982 8和0.985 0,拟合效果明显提升。Pb和Cr元素的训练集均方根误差由0.026 0 Wt%和0.027 2 Wt%下降到0.022 4 Wt%和0.019 1 Wt%,预测集均方根误差由0.101 8 Wt%和0.078 8 Wt% 下降到0.045 8 Wt%和0.042 0 Wt%,模型的稳定性进一步提高。说明PSO-LSSVM算法能够更好地降低土壤基体效应和自吸收效应带来的影响,提高分析结果的精确度与稳定性。     

电感支撑磁绝缘传输线的设计与初步实验

为了研究电感支撑内筒对磁绝缘传输线(MITL)功率流传输的影响,设计了一套电感支撑的MITL及其诊断系统,对MITL各个位置电参数进行了测量,获得了MITL阴阳极电流波形和MITL电压波形。其中一发支撑前阴极电流为90.08 kA,支撑后阴极电流为93.88 kA,阴极电流传输效率为95.96%,多次实验传输效率平均值为92.8%,证实了电感支撑MITL的可行性。     

α‐ZrP/Uracil/Cu2+ nanoparticles as an efficient catalyst in the Morita‐Baylis‐Hillman reaction

A facile synthesis of uracil‐Cu²⁺ nanoparticles immobilized on alpha‐zirconium hydrogen phosphate (α‐ZrP), abbreviated as α‐ZrP/Uracil/Cu²⁺, was presented. This compound was synthesized by the thermal method and used as a reusable catalyst for the Morita‐Baylis‐Hillman reaction without any additives. First, (3‐ iodopropyl) trimethoxysilane as a linker is reacted with α‐ZrP support to give the α‐ZrP/IPTMOS. Addition of uracil and then the addition of copper (II) acetate to α‐ZrP/IPTMOS results in the production of selected catalyst. The Morita‐Baylis‐Hillman reaction catalyzed by α‐ZrP/Uracil/Cu^{2 +} demonstrated high product yield, short reaction time and a straightforward work‐up. The catalyst with enough outside surface was easily recovered using centrifugation and reused five times without a significant reduction in its activity.     

Fly‐Ash‐Supported Synthesis of 2‐Mercaptobenzothiazole Derivatives under Microwave Irradiation

Microwave‐assisted, solvent‐free alkylation and acylation of 2‐mercaptobenzothiazole has been attempted using silica gel, alumina, and a new solid support, fly ash. Fly ash, a waste generated at thermal power stations, could be used as solid support just as efficiently as commercial supports. The additional features of methodology include a much faster reaction, easy workup, higher yields, higher purity of the products, and an ecofriendly approach.     

Hydrogenation of acetylene into ethane–ethene mixtures over modified Pd–alumina catalysts

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