Low silicon non-grain-oriented electrical steel: Linking magnetic properties with metallurgical factors

Commercial supply, from several steel manufacturers, of low-silicon non-grain-oriented electrical steel was monitored over a span of several years. A total of 51 samples were selected—selected from many hundreds on the basis of large differences in magnetic properties, but absence of significant variations in chemistry (other than differences in silicon percentage). The selected samples were analyzed for crystallographic texture and for grain size.     

Rapid and high-capacity ultrasonic assisted adsorption of ternary toxic anionic dyes onto MOF-5-activated carbon: Artificial neural networks,partial least squares,desirability function and isotherm and kinetic study

The present paper focused on the ultrasonic assisted simultaneous removal of fast green (FG), eosin Y (EY) and quinine yellow (QY) from aqueous media following using MOF-5 as a metal organic framework and activated carbon hybrid (AC-MOF-5). The structure and morphology of AC-MOF-5 was identified by SEM, FTIR and XRD analysis. The interactive and main effects of variables such as pH, initial dyes concentration (mg L⁻¹), adsorbent dosage (mg) and sonication time (min) on removal percentage were studied by central composite design (CCD), subsequent desirability function (DF) permit to achieved real variable experimental condition. Optimized values were found 7.06, 5.68, 7.59 and 5.04 mg L⁻¹, 0.02 g and 2.55 min for pH, FG, EY and QY concentration, adsorbent dosage and sonication time, respectively. Under this conditions removal percentage were obtained 98.1%, 98.1% and 91.91% for FG, EY and QY, respectively. Two models, namely partial least squares (PLS) and multi-layer artificial neural network (ANN) model were used for building up to construct an empirical model to predict the dyes under study removal behavior. The obtained results show that ANN and PLS model is a powerful tool for prediction of under-study dyes adsorption by AC-MOF-5. The evaluation and estimation of equilibrium data from traditional isotherm models display that the Langmuir model indicated the best fit to the equilibrium data with maximum adsorption capacity of 21.230, 20.242 and 18.621 mg g⁻¹, for FG, EY and QY, respectively, while the adsorption rate efficiently follows the pseudo-second-order model.     

Simultaneous determination of methylene blue and new methylene blue by slab optical waveguide spectroscopy and artificial neural networks

A slab optical waveguide (SOWG) has been used for study of adsorption of both methylene blue (MB) and new methylene blue (NMB) in liquid-solid interface. Adsorption characteristics of MB and NMB on both bare SOWG and silanized SOWG by octadecyltrichlorosilane (ODS) were compared. The simultaneous determinations of both MB and NMB were explored by flow injection SOWG spectrophotometric analysis and artificial neural networks (ANNs) for the first time. Concentrations of MB and NMB were estimated simultaneously with the ANNs. Results obtained with SOWG were compared with those got by conventional UV-visible spectrophotometry.     

An improved artificial bee colony algorithm for directing orbits of chaotic systems

Artificial bee colony algorithm (ABC) is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we propose an improved ABC (IABC) by using a modified search strategy to generate a new food source in order that the exploration and exploitation can be well balanced and satisfactory optimization performances can be achieved. In addition, to enhance the global convergence, when producing the initial population, both opposition-based learning method and chaotic maps are employed. In this paper, the proposed algorithm is applied to control and synchronization of discrete chaotic systems which can be formulated as both multimodal numerical optimization problems with high dimension. Numerical simulation and comparisons with some typical existing algorithms demonstrate the effectiveness and robustness of the proposed approach.     

Fluorescence optosensor using an artificial neural network for screening of polycyclic aromatic hydrocarbons

This paper presents an optosensor for screening of four polycyclic aromatic hydrocarbons: anthracene (ANT), benzo[a]pyrene (BaP), fluoranthene (FLT), and benzo[b]fluoranthene (Bbf) using a photomultiplier device with an artificial neural network as transducer. The optosensor is based on the on-line immobilization on a non-ionic resin (Amberlite XAD-4) solid support in a continuous flow. The determination was performed in 15 mM H₂PO₄⁻/HPO₄²⁻ buffer solution at pH 7 and 25% of 1,4-dioxane. Feed forward neural networks (multiplayer perceptron) have been trained to quantify the considered Polycyclic aromatic hydrocarbons (PAHs) in mixtures under optimal conditions. The optosensor proposed was also applied satisfactorily to the determination of the considered PAHs in water samples in presence of the other 12 EPA–PAHs.     

半导体纳米复合材料的制备及其光电催化CO2还原

本实验通过模拟植物光合作用,设计制备了新颖的光电联合催化池3D-ZnO/Ni BiVO4/FTO,用电化学沉积法制备了泡沫镍负载的ZnO纳米棒光电阴极和BiVO4光电阳极,以0.1 mol·L^−1 KHCO3水溶液作为电解质,1 mmol·L^−1曙红Y为光敏剂,在−0.6 V硅太阳电池的电压下光电催化还原CO2得到了乙醇、乙酸和甲醇,总产率22.5μmol·L^−1·h^−1·cm^−2。实现了将太阳能贮存为化学能并减少了空气中的CO2,加深了学生对绿色化学和植物Calvin循环机理的理解。     

A graph-theoretic approach to modeling metabolic pathways

Summary The metabolic pathways of medazepam, oxazepam, and diazepam were modeled using graph-theoretic transforms which are incorporable into computer-assisted metabolic analysis programs. The information, represented in the form of a graph-theoretic transform kit, which was obtained from these pathways was then used to predict the metabolites of other benzodiazepine compounds. The transform kits gave statistically significant predictions with respect to a statistical method for evaluating the performance of the transform kits.     

Application of an artificial neural network (ANN) and piezoelectric chemical sensor array for identification of volatile organic compounds

A piezoelectric chemical sensor array was developed using four quartz crystals. Gas chromatographic stationary phases were used as sensing materials and the array was connected to an artificial neural network (ANN). The application of the ANN method proved to be particularly advantageous if the measured property (mass, concentration, etc.) should not be connected exactly to the signal of the transducers of the piezoelectric sensor. The optimum structure of neural network was determined by a trial and error method. Different structures were tried with several neurons in the hidden layer and the total error was calculated. The optimum values of primary weight factors, learning rate (η=0.15), momentum term (μ=0.9), and the sigmoid parameter (β=1) were determined. Finally, three hidden neurons and 900 training cycles were applied. After the teaching process the network was used for identification of taught analytes (acetone, benzene, chloroform, pentane). Mixtures of organic compounds were also analysed and the ANN method proved to be a reliable way of differentiating the sensing materials and identifying the volatile compounds.     

Highly Selective Transmembrane Transport of Exogenous Lithium Ions through Rationally Designed Supramolecular Channels

Lithium ions have been applied in the clinic in the treatment of psychiatric disorders. In this work, we report artificial supramolecular lithium channels composed of pore-containing small aromatic molecules. By adjusting the lumen size and coordination numbers, we found that one of the supramolecular channels developed shows unprecedented transmembrane transport of exogenous lithium ions with a Li⁺/Na⁺ selectivity ratio of 23.0, which is in the same level of that of natural Na⁺ channels. Furthermore, four coordination sites inside channels are found to be the basic requirement for ion transport function. Importantly, this artificial lithium channel displays very low transport of physiological Na⁺, K⁺, Mg²⁺, and Ca²⁺ ions. This highly selective Li⁺ channel may become an important tool for studying the physiological role of intracellular lithium ions, especially in the treatment of psychiatric disorders.     

改进的人工神经网络分光光度法同时测定锆和钛

在锆(钛)-对氯苯基荧光酮-CTMAB显色体系中,应用改进的人工神经网络解析锆和钛的吸收光谱,不经分离分光光度法同时测定锆和钛。在经典的BP算法的基础上改进了传递函数,引用双冲量因子,并对学习速率、动量因子采用自适应调整法,确定了网络的最佳参数。此方法避免了网络陷入过饱和,提高了网络的收敛速度和预测精度,优于经典的BP算法。用于钢样中锆和钛的测定,结果满意。