河北省装备制造产业发展的灰色关联分析

基于灰色系统中的关联分析方法,从动态的视角出发,对河北省装备制造产业生产总值与装备制造产业系统内部结构关系进行研究分析,得出河北省装备制造产业的发展具有以下特点:金属制品业等资源加工型产业依旧在装备制造产业中占据主导地位,通用装备业和仪器仪表业等技术密集型产业的贡献率相对不足.因此,应从河北省的实际情况出发,不断优化装备制造的内部产业结构。     

基于函数系数自回归模型的风速时间序列预测

准确预测风电场风速是解决风能对电力系统所造成的安全、稳定运行和电能质量等问题的有效途径之一.风速的难以预测是由于它的高度随机和非线性.基于一种非参数的非线性自回归随机模型来预测风速,模型的自回归系数随模型依赖变量的变化而变化,因而它有灵活的非线性结构.数值实验和比较结果表明了这种函数系数自回归模型在风电场风速预测中的有效性.     

基于Leslie模型的北京市基础教育学龄人口分城乡预测

以2010年第六次人口普查数据为基点,考虑到已经开放的单独二胎政策以及全面放开的二胎政策等因素,对北京市2020—2030年基础教育学龄人口变动情况分城乡进行预测.研究发现:北京学龄人口总体上呈现先上升后下降趋势;城市学龄人口远远高于农村学龄人口;城市各个阶段学龄人口均出现了先升后降趋势;农村幼儿园学龄人口呈现快速下降趋势,小学、初中学龄人口均呈现了先升后降趋势,农村高中学龄人口呈现了"下降-上升-下降-上升"的反复波动.学龄人口的变化反映了未来教育需求的变动,在未来10-15年,北京市还需要加大教师队伍建设、加强基础设施建设,努力实现城乡教育资源均衡化,最大限度地实现教育公平.     

基于模糊灰色新陈代谢FGM(1,1)模型预测及应用探讨

巷道围岩变形是煤矿开采普遍存在的问题,巷道开挖过程中围岩会出现破裂现象.主要探讨模糊灰色系统的深部巷道围岩变形预测模型及应用,通过分析模糊数学理论、灰色理论,给出模糊灰色系统预测新陈代谢模型.     

Reaction mechanism of hydrogen cyanide catalyzed by gas‐phase titanium

To explore the details of the reaction mechanism of Ti atom with HCN, the reactive site and reactivity have been predicted first, the potential energy surfaces have been systematically studied at different theoretical levels. Four different reaction pathways and product distribution are discussed by means of the activation strain model and Curtin–Hammett principle. In addition, the structures, bonding properties and the frontier molecular orbital interaction diagrams of main stationary points were analyzed by atoms in molecules and natural bond orbital. The results show that for this system, there are four reaction pathways, in which path b (HCN＋Ti→IM1→TS1→IM2→T2b→IM4) is the most favorable pathway.     

Solubility prediction of deferiprone in N-methyl-2-pyrrolidone + ethanol mixtures at various temperatures using a minimum number of experimental data

Experimental solubility of deferiprone (DFP) in N-methyl-2-pyrrolidone (NMP) + ethanol (EtOH) mixtures at 293.2, 298.2, 303.2 and 308.2 K was determined and mathematically represented using various models. The trained versions of the van’t Hoff equation, its combined version with log-linear model, Jouyban–Acree model and a combination of van’t Hoff + Jouyban–Acree model were reported to simulate DFP solubility in the binary mixture compositions at various temperatures. The mean percentage deviation (MPD) was used as an accuracy criterion. The obtained overall MPDs for back-calculated and predicted solubility of DFP in NMP + EtOH mixtures varied from 1.1% to 3.2% and 2.6% to 6.6%, respectively. Some of apparent thermodynamic quantities for the dissolution processes of DFP are also reported.     

Theoretical approaches for predicting the color of rigid dyes in solution

Aiming at developing an affordable and easily implementable computational protocol for routine prediction of spectral properties of rigid molecular dyes, density functional theory, and time‐dependent density functional theory were used in conjunction with a vibronic coupling scheme for band shape estimate. To predict the perceived color of molecules in solution, a model has been setup linking the UV‐vis spectra predicted at ab initio level to the L *a *b * colorimetric parameters. The results show that a mixed protocol, implying the use of a global hybrid functional for the prediction of adiabatic energy differences and a range separated hybrid for the prediction of potential energy curvature, allows perceived colors to be quantitatively predicted, as demonstrated by the comparison of L *a *b * colorimetric parameters obtained from computed and experimental spectra. © 2017 Wiley Periodicals, Inc.     

Microdroplet size prediction in microfluidic systems via artificial neural network modeling for water-in-oil emulsion formulation

In this paper, an experimental study and modeling by artificial neural networks were carried out to predict the generated microdroplet dimensionless size in a microfluidic system in order to formulate a water-in-oil emulsion. The various parameters that affect the size of microdroplets (flow rates, viscosities, surface tensions of both the two phases and the diameter of the microchannel) are studied and further grouped into dimensionless numbers; we used these numbers as input to the neural network and the dimensionless length as output. The better neural network architecture has 10 neurons in the hidden layer with a mean square error of 1.4 10^?6 and a determination’s coefficient near 1 value. The relative importance of inputs on the size of the microdroplets has been determined using the Garson algorithm and the results are in good agreement with other works.     

Full Bayesian analysis of claims reserving uncertainty

We revisit the gamma–gamma Bayesian chain-ladder (BCL) model for claims reserving in non-life insurance. This claims reserving model is usually used in an empirical Bayesian way using plug-in estimates for the variance parameters. The advantage of this empirical Bayesian framework is that allows us for closed form solutions. The main purpose of this paper is to develop the full Bayesian case also considering prior distributions for the variance parameters and to study the resulting sensitivities.     

A self‐adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application

The self‐adaptive intelligence gray predictive model (SAIGM) has an alterable‐flexible model structure, and it can build a dynamic structure to fit different external environments by adjusting the parameter values of SAIGM. However, the order number of the raw SAIGM model is not optimal, which is an integer. For this, a new SAIGM model with the fractional order accumulating operator (SAIGM_FO) was proposed in this paper. Specifically, the final restored expression of SAIGM_FO was deduced in detail, and the parameter estimation method of SAIGM_FO was studied. After that, the Particle Swarm Optimization algorithm was used to optimize the order number of SAIGM_FO, and some steps were provided. Finally, the SAIGM_FO model was applied to simulate China's electricity consumption from 2001 to 2008 and forecast it during 2009 to 2015, and the mean relative simulation and prediction percentage errors of the new model were only 0.860% and 2.661%, in comparison with the ones obtained from the raw SAIGM model, the GM(1, 1) model with the optimal fractional order accumulating operator and the GM(1, 1) model, which were (1.201%, 5.321%), (1.356%, 3.324%), and (2.013%, 23.944%), respectively. The findings showed both the simulation and the prediction performance of the proposed SAIGM_FO model were the best among the 4 models.