Co-, Fe-, Ni-doped and co-doped rutile GeO2: insights from ab-initio calculations

Rutile germanium oxide (rutile GeO₂), a semiconductor, can act as a half-metallic compound and is a promising material for spintronic and optoelectronic applications. Calculations were performed using the Korringa–Kohn–Rostoker (KKR) approach and the coherent potential approximation (CPA), which were further combined with two approximations, the local density approximation (LDA) and the self-interaction corrected LDA approximation (LDA-SIC), to study the electronic structure of bulk rutile GeO₂ doped and co-doped with three transition-metal impurities: Fe, Co, and Ni. The doping value was set to 10%, while the co-doping level was set to 5% for each impurity. The main findings of this work are: (1) a direct ultrawide bandgap of 4.80 eV is observed and the rutile GeO₂ exhibits an N-type semiconducting property. (2) Doped and co-doped GeO₂ acquire a magnetic behavior and exhibit half-metallicity. (3) The mechanism responsible for these properties is also studied. (4) The critical temperature can reach 334 K when GeO₂ is doped with Fe, while it rises to 398 K when it is co-doped with Fe and Co. (5) The spin polarization can be improved by co-doping. It can be inferred that rutile GeO₂ doped or co-doped with (Co, Fe) transition metals can be considered to be potential candidates for spintronic and optoelectronic applications.     

基于三维荧光光谱技术对掺假蜂蜜无损鉴别研究

提出了一种应用三维荧光谱技术结合化学计量学方法快速无损鉴别蜂蜜中大米糖浆掺假的新方法。利用特征参量法和主成分分析法对三维荧光光谱信息量进行压缩提取,并结合线性判别分析法(LDA)和误差反向传播神经网络法(BP-ANN)对蜂蜜掺假进行分析。结果显示,在掺假蜂蜜判别试验中,采用4个主成分时,模型对预测集样本的识别率最佳,LDA模型识别率为94.44%,BP-ANN模型识别率为100%,说明非线性的BP-ANN模型更适合蜂蜜掺假识别。研究表明,三维荧光光谱结合BP-ANN判别模型可以快速、 无损、 准确地鉴别蜂蜜中大米糖浆掺假。     

不同波长提取方法的高光谱成像技术检测番茄叶片早疫病的研究

提出了基于连续投影算法(successive projections algorithm,SPA)、载荷系数法(x-loading weights,x-LW)和格拉姆-施密特正交(gram-schmidt orthogonalization,GSO)提取特征波长的高光谱成像技术检测番茄叶片早疫病的方法。首先获取380～1 023 nm波段范围内70个健康和70个染病番茄叶片的高光谱图像信息,然后提取健康和染病叶片感兴趣区域(region of interest, ROI)的光谱反射率值,建立番茄叶片早疫病的最小二乘-支持向量机(least squares-support vector machine,LS-SVM)鉴别模型,建模集和预测集的鉴别率都是100%。再通过SPA 、x-LW和GSO提取特征波长(effective wavelengths,EW),并建立EW-LS-SVM和特征波长-线性判别分析(ew-linear discriminant analysis,EW-LDA)鉴别模型。结果显示,每个模型的鉴别效果都很好,EW-LS-SVM模型中预测集的鉴别率都达到了100%,EW-LDA模型中预测集的鉴别率分别是100%, 100%和97.83%。基于SPA, x-LW和GSO所建模型的输入变量分别是4个(492,550,633和680nm),3个(631,719和747 nm)和2个(533和657 nm),较少的特征波长便于实时检测仪器的开发。结果表明,高光谱成像技术检测番茄叶片早疫病是可行的,SPA,x-LW和GSO都是非常有效的特征波长提取方法。     

Fusion (2D)PCALDA: A new method for face recognition

This paper proposes an efficient face representation and recognition method, which combines the both information between rows and those between columns from two-directional 2DPCA on fusion face image and the optimal discriminative information from column-directional 2DLDA. Experiment results on ORL and Yale face database demonstrate the effectiveness of the proposed method.     

双激光二极管阵列侧面交错抽运的电光调Q Nd:YAG激光器

采用双激光二极管阵列(LDA)侧面交错抽运Nd:YAG晶体,通过电光调Q的方式获得1064 nm动态脉冲激光输出.这种抽运结构可以使晶体内的增益场与谐振腔基模实现良好的匹配,易于得到良好的光束质量和大能量输出.抽运源采用峰值功率为100 W的准连续LDA,采用直接贴近抽运的方式,KD*P晶体作为电光Q开关.并应用高斯光束传输的ABCD定律,计算了谐振腔稳区范围,给出了较为合理的谐振腔参数.所设计的激光器在重复频率20 Hz,抽运能量1200 mJ时,获得了最大输出能量151 mJ,脉宽8.48 ns的1064 nm动态激光输出,光-光转换效率为12.6%.     

Effect of particle size on a two-phase turbulent jet

The effect of particle size on two-phase turbulent jet flow structure is studied in the present experimental investigation. Polystyrene solid particles of 210, 460, and 780 μm were used. The particles' mass loading ratios ranged from 0 to 3.6. The flow Reynolds number was 2 ‘ 10⁴, which was based on the pipe nozzle diameter and the fluid-phase centerline velocity at the nozzle exit. A two-color laser-Doppler anemometer (LDA), combined with the amplitude discrimination method and the velocity filter method, was employed for measurement. The measurement range of the jet flow was from the initial pipe exit to 90D downstream. Results are presented for the mean velocities of particle and fluid phases, the flow's turbulent intensities and the flow's Reynolds stresses. The energy spectra and the correlation functions of the two-phase jet flow were also obtained by using another one-component He-Ne LDA system.     

Prediction of protein structural class based on symmetrical recurrence quantification analysis

Protein structural class prediction for low similarity sequences is a significant challenge and one of the deeply explored subjects. This plays an important role in drug design, folding recognition of protein, functional analysis and several other biology applications. In this paper, we worked with two benchmark databases existing in the literature (1) 25PDB and (2) 1189 to apply our proposed method for predicting protein structural class. Initially, we transformed protein sequences into DNA sequences and then into binary sequences. Furthermore, we applied symmetrical recurrence quantification analysis (the new approach), where we got 8 features from each symmetry plot computation. Moreover, the machine learning algorithms such as Linear Discriminant Analysis (LDA), Random Forest (RF) and Support Vector Machine (SVM) are used. In addition, comparison was made to find the best classifier for protein structural class prediction. Results show that symmetrical recurrence quantification as feature extraction method with RF classifier outperformed existing methods with an overall accuracy of 100% without overfitting.     

Review of validation and reporting of non-targeted fingerprinting approaches for food authentication

Food fingerprinting approaches are expected to become a very potent tool in authentication processes aiming at a comprehensive characterization of complex food matrices. By non-targeted spectrometric or spectroscopic chemical analysis with a subsequent (multivariate) statistical evaluation of acquired data, food matrices can be investigated in terms of their geographical origin, species variety or possible adulterations. Although many successful research projects have already demonstrated the feasibility of non-targeted fingerprinting approaches, their uptake and implementation into routine analysis and food surveillance is still limited. In many proof-of-principle studies, the prediction ability of only one data set was explored, measured within a limited period of time using one instrument within one laboratory. Thorough validation strategies that guarantee reliability of the respective data basis and that allow conclusion on the applicability of the respective approaches for its fit-for-purpose have not yet been proposed. Within this review, critical steps of the fingerprinting workflow were explored to develop a generic scheme for multivariate model validation. As a result, a proposed scheme for “good practice” shall guide users through validation and reporting of non-targeted fingerprinting results. Furthermore, food fingerprinting studies were selected by a systematic search approach and reviewed with regard to (a) transparency of data processing and (b) validity of study results. Subsequently, the studies were inspected for measures of statistical model validation, analytical method validation and quality assurance measures. In this context, issues and recommendations were found that might be considered as an actual starting point for developing validation standards of non-targeted metabolomics approaches for food authentication in the future. Hence, this review intends to contribute to the harmonization and standardization of food fingerprinting, both required as a prior condition for the authentication of food in routine analysis and official control.     

用PCA-LDA对多环芳烃的致癌性进行分类

本文利用主成分提取-线性判别分析(PCA-LDA)模型对多环芳烃(Polycyclic Aromatic Hydrocarbons,PAHs)的致癌性进行分类,与致癌性有关的多环芳烃的表面积、代谢活性区域中心碳原子的离域能、亲电活性区域中心碳原子的离域能以及分子脱毒区的总数四个参数作为模型的输入,用已知致癌性的67个样本作为训练集建立PCA-LDA模型,对10个预测集样本的致癌性进行预测,结果表明:致癌性按高(h)、低(l)、非(n)分类时预测准确率达100%。     

Ab initio calculation of dynamic polarizability and dielectric constant of carbon and silicon cubic crystals

Valence and conduction bands of carbon silicon cubic systems are first obtained by a process called linear combination of atomic orbitals self-consistent field (LCAO-SCF), both at the Hartree-Fock (HF) and local density approximation (LDA) levels. Then, the crystalline orbitals are used in a sum-over-states (SOS) method to calculate the corresponding dielectric constants related to electronic polarizabilities. This method allows parallel computations with large granularity of the optical properties and leads to uncoupled HF and LDA results. © 1997 John Wiley & Sons, Inc. J Comput Chem 18: 1253–1263, 1997