Arsenidostannate mit Arsen-analogen [SnAs]-Schichten: Darstellung und Struktur von Na[Sn2As2], Na0,3Ca0,7[Sn2As2], Na0,6Sr0,6[Sn2As2], Na0,6Ba0,4[Sn2As2] und K0,3Sr0,7[Sn2As2]

Arsenidostannates with [SnAs] Nets Isostructural to Grey Arsenic: Synthesis and Crystal Structure of Na[Sn₂As₂], Na_0.3Ca_0.7[Sn₂As₂], Na_0.4Sr_0.6[Sn₂As₂], Na_0.6Ba_0.4[Sn₂As₂], and K_0.3Sr_0.7[Sn₂As₂] The metallic lustrous compounds Na[Sn₂As₂], Na_0.3Ca_0.7[Sn₂As₂], Na_0.4Sr_0.6[Sn₂As₂], Na_0.6Ba_0.4[Sn₂As₂] and K_0.3Sr_0.7[Sn₂As₂] were prepared from melts of mixtures of the elements. The compounds crystallize in the trigonal system (space group R3 m, No. 166, Z = 3) with lattice constants see in “Inhaltsübersicht”. The structures are isotypic to Sr[Sn₂As₂] containing puckered [SnAs] nets which are stacked with a sequence of six layers. The E(I)/E(II) atoms are located between each second [SnAs] layer in trigonal antiprismatic interstices formed by As atoms. In the resulting [Sn₂As₂] double layers the _∞²[SnAs] nets are stacked in such a way that additional Sn—Sn contacts arise.     

A study for quality evaluation of Taxilli Herba from different hosts based on fingerprint-activity relationship modeling and multivariate statistical analysis

In this study, a fingerprint-activity relationship modeling between chemical fingerprints and antirheumatic activity was established, and multivariate statistical analysis was used to evaluate the quality of Taxilli Herba (TH) from different hosts. Characteristic fingerprints of 20 batches of TH samples were generated by high-performance liquid chromatography coupled with triple quadrupole-time of flight tandem mass spectrometry (HPLC-Triple TOF-MS/MS), and the similarity analysis was calculated based on thirteen common characteristic peaks by hierarchical clustering analysis (HCA). Subsequently, nine efficacy markers were discovered by combining fingerprints and antirheumatic activity through grey correlation analysis (GCA) and bivariate correlation analysis (BCA). Meanwhile, the content of 5 constituents in 9 markers was determined by high-performance liquid chromatography coupled with triple quadrupole-linear ion trap tandem mass spectrometry (HPLC-QTRAP-MS/MS). The comprehensive quality of TH was assessed using multivariate statistical analysis, including principal components analysis (PCA) and technique for order preference by similarity to ideal solution (TOPSIS). The results showed that a high dose of TH extract could markedly ameliorate arthritis damage compared to other doses, with flavonoids playing an important role in the antirheumatic activity. The comprehensive quality of samples from Morus alba L. (SS) was superior to those from Liquidambar formosana Hance (FXS). The present study will demonstrate the markers associated with efficacy, and provide an applicable strategy for more comprehensive quality control and evaluation of TH.     

基于灰色数学的洗胃液量的软测量

鉴于洗胃液量实时在线检测与求解的困难,提出利用灰色数学的累加生成方法,把液量作为主导变量,管路二处压力差的累积压力作为二次变量,根据二者之间的一一映射关系,仿真实验研究洗胃液量的软测量技术,给出了实验结果,为液量的工程检测提供了一种数学方法.     

基于灰色聚类分析的风险评价方法研究——以江苏民间资本进入金融领域风险评价为例

运用灰色系统理论,结合AHP方法,建立灰色聚类分析模型,对风险进行评价,并以江苏民间资本进入金融领域风险评价为例进行研究.结果表明,民间资本进入金融领域的风险属于高风险范畴,其中进入风险中的产业风险和社会风险属于高风险,信用风险属于较高风险,政治风险属于中等风险.并针对上述风险的等级,提出了相应的政策建议.模型具有一定的实用价值,它对于正确认识民间资本进入金融领域的风险和有针对性地制定相应的政策措施具有一定的理论和现实意义.     

灰色Verhulst神经网络组合预测区域空域流量

能够对区域空域的流量做出准确的预测,可以为空中交通管理部门协调航班运行,减缓空域内航路的拥堵提供重要依据.运用灰色Verhulst模型与神经网络的组合模型对区域航班流量进行预测,并结合华东地区飞行流量的数据样本训练并测试模型.结果显示,由组合模型得出的流量预测值与实际值较吻合,说明组合模型比单种预测方法能够更为准确地对区域空域流量做出预测.     

The multi-attribute grey target decision method for attribute value within three-parameter interval grey number

Based on the grey system theory and methods, the grey-target decision-making problem is discussed, in which the attribute values are grey numbers and the maximum probability of the value of grey number is known. Firstly, the optimal effect vector is the positive bull’s-eye and positive bull’s-eye distance of each scheme is defined. Subjectively or objectively weighting method is integrated to determine the index weight and integrated optimization model of index weight is established. Finally, the critical effect vector is the negative bull’s-eye and negative bull’s-eye distance of each scheme is defined, then relative bull’s-eye distance and comprehensive the bull’s-eye distance of grey target decision-making are given. An example is also presented to illustrate the usefulness and effectiveness of the methods obtained in this paper and provides a new idea for grey target decision-making method research.     

基于改进极根学习机的回转窑煅烧带温度预测方法

针对传统算法预测回转窑煅烧带温度存在精度低、速度慢的问题,提出了基于改进极限学习机(ELM)的回转窑煅烧带温度预测方法。对ELM输入权值矩阵定义了变换系数,采用黄金分割法在给定区间内搜寻变换系数的最佳值,改进了ELM网络参数的确定方式,弥补了随机确定输入权值并且不作调整的缺陷,在保证ELM训练速度的前提下提高预测精度、减小模型随机性。实验结果表明,改进的ELM预测精度高、训练速度快、模型性能优,可满足工况恶劣的回转窑的生产需要。     

基于Chebyshev多项式的神经网络中长期负荷预测研究

高精度负荷预测在提高电力系统的安全性和经济性方面有着极其重要的意义,而现有的负荷预测方法因参数有限,难以完全反映其内在规律,因而导致预测结果不够准确.为此提出了一种基于Chebyshev多项式神经网络模型的预测方法.该方法使用递推最小二乘法训练神经网络权值系数,以获得高精度的参数估计,从而实现Chebyshev多项式神经网络模型对负荷量的最优拟合,再利用训练好的Chebyshev多项式神经网络模型实现中长期负荷预测.研究结果表明,该方法能较好模拟负荷变化规律,有效提高了负荷预测精度,在电力系统负荷预测中有较大的应用价值.     

广东省能源需求预测模型构建及实证分析

为了对广东省的能源需求进行准确的预测,首先分析了影响广东省能源需求的各种因素,构建了预测指标体系.在此基础上,针对能源系统非线性等复杂系统特征,结合粒子群算法和BP神经网络的优点,构建了改进的PSO-BP神经网络的预测模型,并通过主成分分析法对指标体系进行数据降维,以降低神经网络的规模和复杂程度.以广东省1985-2013年的能源需求数据进行模拟与仿真,并对2014-2018年的能源需求量进行预测,理论分析和实证研究表明,该方法能够很好的反映广东省能源需求的特征,预测结果较为准确合理.     

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

The stock index is an important indicator to measure stock market fluctuation, with a guiding role for investors’ decision-making, thus being the object of much research. However, the stock market is affected by uncertainty and volatility, making accurate prediction a challenging task. We propose a new stock index forecasting model based on time series decomposition and a hybrid model. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the stock index into a series of Intrinsic Mode Functions (IMFs) with different feature scales and trend term. The Augmented Dickey Fuller (ADF) method judges the stability of each IMFs and trend term. The Autoregressive Moving Average (ARMA) model is used on stationary time series, and a Long Short-Term Memory (LSTM) model extracts abstract features of unstable time series. The predicted results of each time sequence are reconstructed to obtain the final predicted value. Experiments are conducted on four stock index time series, and the results show that the prediction of the proposed model is closer to the real value than that of seven reference models, and has a good quantitative investment reference value.