Optimization strategy based on genetic algorithms and neural networks applied to a polymerization process

The article presents a simple and general methodology, especially destined to the optimization of complex, strongly nonlinear systems, for which no extensive knowledge or precise models are available. The optimization problem is solved by means of a simple genetic algorithm, and the results are interpreted both from the mathematical point of view (the minimization of the objective function) and technological (the estimation of the achievement of individual objectives in multiobjective optimization). The use of a scalar objective function is supported by the fact that the genetic algorithm also computes the weights attached to the individual objectives along with the optimal values of the decision variables. The optimization strategy is accomplished in three stages: (1) the design and training of the neural model by a new method based on a genetic algorithm where information about the network is coded into the chromosomes; (2) the actual optimization based on genetic algorithms, which implies testing different values for parameters and different variants of the algorithm, computing the weights of the individual objectives and determining the optimal values for the decision variables; (3) the user's decision, who chooses a solution based on technological criteria. © 2007 Wiley Periodicals, Inc. Int J Quantum Chem, 2008     

Characterization of oxidation progress by chemiluminescence: A study of polyethylene with pro-oxidant additives

Non-isothermal chemiluminescence measurements in nitrogen and isothermal measurements in oxygen were used for the evaluation of degradation in pre-oxidized polyethylene either pure or containing Mn-based pro-oxidant additives. The results were compared with infrared spectroscopy data. Chemiluminescence measurements of pure polyethylene and polyethylene with additive made it possible to calculate the set of rate constants, based on the Bolland-Gee oxidation scheme. The oxidation rate constants of polyethylene with additive were significantly higher, while the activation energy of the process appeared lower (65 kJ mol⁻¹), than those of pure polyethylene. The method provides an access to study oxidation processes during the induction period of oxidation when infrared spectroscopy cannot provide sufficient information.     

Artificial neural network for Cu quantitative determination in soil using a portable Laser Induced Breakdown Spectroscopy system

Laser Induced Breakdown Spectroscopy (LIBS) is an advanced analytical technique for elemental determination based on direct measurement of optical emission of excited species on a laser induced plasma. In the realm of elemental analysis, LIBS has great potential to accomplish direct analysis independently of physical sample state (solid, liquid or gas). Presently, LIBS has been easily employed for qualitative analysis, nevertheless, in order to perform quantitative analysis, some effort is still required since calibration represents a difficult issue. Artificial neural network (ANN) is a machine learning paradigm inspired on biological nervous systems. Recently, ANNs have been used in many applications and its classification and prediction capabilities are especially useful for spectral analysis. In this paper an ANN was used as calibration strategy for LIBS, aiming Cu determination in soil samples. Spectra of 59 samples from a heterogenic set of reference soil samples and their respective Cu concentration were used for calibration and validation. Simple linear regression (SLR) and wrapper approach were the two strategies employed to select a set of wavelengths for ANN learning. Cross validation was applied, following ANN training, for verification of prediction accuracy. The ANN showed good efficiency for Cu predictions although the features of portable instrumentation employed. The proposed method presented a limit of detection (LOD) of 2.3 mg dm^− 3 of Cu and a mean squared error (MSE) of 0.5 for the predictions.     

一个环硫乙烷中间体的合成

为了获得制备超高折光树脂的单体, 合成了一个能够有效制备环高折光树脂的硫乙烷中间体——(甲氧基羰基二硫化)-环硫乙烷. 该中间体可以和硫醇在温和的条件下生成环硫单体. 和二巯基甲烷反应能生成2,2’-(亚甲基双二硫化)-双环硫乙烷单体, 聚合后得到了折光指数和阿贝数高达nD/νD＝1.79/28的热固性光学树脂.     

T型微混合器混合特性的浓度分布评价法

提出一种微混合器混合性能的评价方法.在样品盒中注入不同浓度罗丹明B溶液并用体视显微镜观察捕获图像,通过Image J软件读取图像灰度值,建立不同深度下溶液的浓度-灰度值函数关系,运用此关系式将T型微混合器3种不同深度(0.1、 0.2和0.4 mm,质量浓度0.05 %的罗丹明B溶液和去离子水作为配对流体)混合实验中捕获的图像中各像素点上的灰度值转换为浓度值,绘制浓度等高线图及浓度频数分布图,分析各自混合情况,最后引入浓度混合指数概念及计算公式,分析3种深度混合器内不同截面上的混合程度.此方法从定性和定量两方面分析了微尺度下混合腔深度对微混合的影响程度,具有一定的应用价值.     

Kekulé structures of hexagonal chains—some unusual connections

It is known since 1977 that the number K of Kekulé structures of a hexagonal chain is equal to the topological Z-index of a pertinently constructed “caterpillar” tree. Based on this relation we now connect K with some of other, seemingly unrelated, concepts: continuants (from number theory) and matchings of the path–graph (further related to Fibonacci numbers). We also arrive at a tridiagonal determinantal expression for K.     

The smallest Hosoya index in (<Emphasis Type="Italic">n</Emphasis>, <Emphasis Type="Italic">n</Emphasis> + 1)-graphs

A (n, n + 1)-graph G is a connected simple graph with n vertices and n + 1 edges. In this paper, we determine the lower bound for the Hosoya index in (n, n + 1)-graphs in terms of the order n, and characterize the (n, n + 1)-graph with the smallest Hosoya index.     

二元无机物标准熵 So298的拓扑研究

为了预测二元无机物的标准熵,基于分子图的连接矩阵和离子参数gi、qi,提出了一种新的连接性指数mQ, mG及其逆指数mQ’, mG’。 qi、gi定义为：qi=(1.1+Zi1.1) /(1.7+ni), gi=(1.4+Zi) /(0.9+ri+ri-1),其中Zi 、ni和 ri分别代表离子i的电荷数、最外层主量子数和半径。从0Q, 0Q’, 1G,和1G’,利用多元线性回归分析方法和人工神经网络方法,可以构建优良的QSPR模型。对371个二元无机物,其多元线性模型及神经网络模型的相关系数、标准偏差和平均绝对偏差分别是：0.9905, 8.29 J.K-1.mol-1, 6.48 J. K-1.mol-1, 0.9960, 5.37 J.K-1.mol-1 和 3.90 J.K-1.mol-1。留一法交叉验证表明,其多元线性模型具有良好的稳定性。两个模型对187个未进入模型的二元无机物的标准熵的预测值和实验值之间的相关系数、标准偏差和平均绝对偏差分别是：0.9897, 8.64 J. K-1.mol-1, 6.84 J. K-1.mol-1, 0.9957, 5.63 J.K-1.mol-1, 和 4.18 J.K-1.mol-1。研究表明,本文方法在预测二元无机物标准熵时比文献方法更有效,两种模型均能较精确的预测二元无机物的标准熵,且神经网络模型的预测结果更精确。     

有机污染物的生物富集因子与拓扑指数的数学模型

依据价连接性指数(xi)、电性拓扑态指数(ej)及电性距离矢量(mk)构建239种有机污染物生物富集因子(FBC)的6参数QSFR(定量结构-生物富集因子相关性)模型, 不仅相关程度高, 而且所用自变量数少. 该模型的传统相关系数(R2)为0.821, LOO(levae-one-out)交互验证系数(Q2)为0.809, 证明具有良好的稳健性及预测能力. 根据进入该模型的6个参数可知, 影响有机污染物FBC的主要结构基团是: —C—、>C—、—O—、—X、—NH2以及分子的柔韧性、折叠程度等空间因素. 可以认为在生物富集过程中,“诱导契合机理”也发挥一定作用. 对自变量集进行正交变换, 既不影响模型的相关性, 又能降低其自相关性.     

Films Consisting of Innumerable Tapered Nanopillars of Mesoporous Silica for Universal Antireflection Coatings

Films with a fine structure consisting of innumerable nanopillars of mesoporous silica (MPS) are formed by a reactive ion etching process with a fluorine‐containing gas. Each nanopillar has a tapered shape with a uniform height, which effectively suppresses reflection by the formation of an ideal graded refractive index structure. The nanopillars are spontaneously formed under low‐pressure conditions, wherein locally deposited Al?F compounds, originating from an alumina plate in the etching chamber, work as a fine etching mask. The high etching rate of the MPS film allows a very high aspect ratio of the nanopillars. The refractive index of the MPS nanopillars can be universally tuned by a controlled incorporation of TiO₂ into the mesopores, which results in effective reduction of reflectance on a given substrate. The outstanding antireflection performance is experimentally demonstrated for glass substrates with a wide refractive index range.