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.     

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。     

The Orion Water-Hardness Electrode: An Unexpected Response to Magnesium Ion

Abstract

The Orion water-hardness electrode, while being used to measure magnesium ion activity in a series of calcium-free solutions, displayed some unanticipated changes in potential. As the overall potential change observed for one solution was 183 mV (equivalent to more than six orders of magnitude changes in magnesium-ion activity), several precautions are suggested which should be observed when using this electrode for specific purposes. In particular, it is recommended that the exchanger be pre-equilibrated with an appropriate solution if the electrode is to be used in restricted systems of known chemical composition.     

Effect of long-period stacking ordered phase on thermal stability of refined grains in Mg-RE-based alloys

The refined grains in both as-cast Mg-10Y-1Zn-2Zr (WZ101?K, wt.%) and Mg-10Y-1Al (WA101, wt.%) alloys have been found to have superior thermal stability after solution treatment. Plate-shaped, but different polytypes of long-period stacking ordered (LPSO) phases distributed along grain boundaries are considered as the predominant inhibitors of grain growth. Based on the plate-shaped features of the LPSO phases, a modified Zener’s model is established and the predicted limiting grain sizes are in a good agreement with the experimental results. Therefore, this model has strong potential for designing advanced Mg-RE-based alloys with high strength and high creep resistance at high temperatures.     

A simple zero length surface-modification approach for preparing novel bifunctional supports for co-immobilisation studies

A straightforward method of creating a bifunctional support possessing pre-defined ratios of amine and chloromethyl groups based upon a controlled conversion of standard Merrifield resin is presented. It avoids the extensive optimisation of reactant concentrations and reaction conditions required by existing methods. Furthermore, it avoids increases in the spacer/linker length that lead to detrimental reductions in the rigidity of the grafted layer. The method will allow for efficient, predictable and reproducible co-immobilisation of molecular species.     

高掺镁LiNbO3晶体折射率温度系数的表示式

推导了掺５ｍｏｌ％ＭｇＯ的ＬｉＮｂＯ３晶体折射率温度系数的表示式。利用这些表示式可以计算２９３～４２８Ｋ温度和０．５３９８μｍ～１．３４１４μｍ波长范围内的折射率温度系度，结果表明：计算值和实验值的最大相对偏差是１２％，用具有最大相对偏差的折射率温度系数的计算值，计算１．０７９５μｍ波长的非临界相位匹配温度，其值为３８２．４Ｋ，它与实验值仅差６Ｋ。因此，本文得到的表示式，对于采用这种晶体，设计在上     

镓一镁二元相图研究

本文用两种差热分析方法即“一步法”和“二步法”以及多晶X-射线衍射法对镓-镁二元系相图进行了研究.修正了前人的工作. 为了考察该二元体系的金属间化合物是否以镓原子多面体簇状结构存在,作者对富镓部分相图(镓原子百分含量大于71.4％)进行了较为细致的研究.在三种不同比例下(含镓量分别为85at.％、90at.％、95at.％)合成,分离得到了颗粒状晶体.对这些晶体进行了差热分析和多晶X-射线研究.分析结果表明:从这三种不同比例的合金中分离得到的晶体为同一金属间化合物Mg_2Ga_5,也是该二元系镓含量最高的金属间化合物.考察Ga-Mg体系中存在的五种金属间化合物的结构,未能发现镓原子以多面体簇状结构存在的现象.     

MgCu4Sn型稀土镁合金相力学和热力学性质的第一性原理研究

采用基于密度泛函理论的第一性原理计算方法,系统研究了典型立方结构合金相LaMgX4(X=Co, Ni, Cu)的力学特性和热力学特性。根据广义胡可定律计算了合金相的单晶弹性常数;根据单晶弹性常数和Hershey’s averaging方法计算了合金相的多晶弹性模量、泊松比、Zener各向异性因子和德拜温度。采用基于准谐近似的Gibbs2代码计算了LaMgX4(X=Co, Ni, Cu)合金相的吉布斯自由能、熵和等体热容与温度的关系。LaMgNi4的计算结果与其他文献计算结果及实验结果符合的很好。结果表明：LaMgX4 (X= Co, Ni, Cu)合金均为延展性、塑性和弹性各向异性材料。德拜温度按以下顺序递减：LaMgNi4>LaMgCu4>LaMgCo4。     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.