Monte Carlo simulation of polarizable systems: Early rejection scheme for improving the performance of adiabatic nuclear and electronic sampling Monte Carlo simulations

An early rejection scheme for trial moves in adiabatic nuclear and electronic sampling Monte Carlo simulation (ANES-MC) of polarizable intermolecular potential models is presented. The proposed algorithm is based on Swendsen–Wang filter functions for prediction of success or failure of trial moves in Monte Carlo simulations. The goal was to reduce the amount of calculations involved in ANES-MC electronic moves, by foreseeing the success of an attempt before making those moves. The new method was employed in Gibbs ensemble Monte Carlo (GEMC) simulations of the polarizable simple point charge-fluctuating charge (SPC-FQ) model of water. The overall improvement in GEMC depends on the number of swap attempts (transfer molecules between phases) in one Monte Carlo cycle. The proposed method allows this number to increase, enhancing the chemical potential equalization. For a system with 300 SPC-FQ water molecules, for example, the fractions of early rejected transfers were about 0.9998 and 0.9994 at 373 and 423 K, respectively. This means that the transfer moves consume only a very small part of the overall computing effort, making GEMC almost equivalent to a simulation in the canonical ensemble.     

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.     

A polarizable ellipsoidal force field for halogen bonds

The anisotropic effects and short‐range quantum effects are essential characters in the formation of halogen bonds. Since there are an array of applications of halogen bonds and much difficulty in modeling them in classical force fields, the current research reports solely the polarizable ellipsoidal force field (PEff) for halogen bonds. The anisotropic charge distribution was represented with the combination of a negative charged sphere and a positively charged ellipsoid. The polarization energy was incorporated by the induced dipole model. The resulting force field is “physically motivated,” which includes separate, explicit terms to account for the electrostatic, repulsion/dispersion, and polarization interaction. Furthermore, it is largely compatible with existing, standard simulation packages. The fitted parameters are transferable and compatible with the general AMBER force field. This PEff model could correctly reproduces the potential energy surface of halogen bonds at MP2 level. Finally, the prediction of the halogen bond properties of human Cathepsin L (hcatL) has been found to be in excellent qualitative agreement with the cocrystal structures. © 2013 Wiley Periodicals, Inc.     

Quantification of fructo-oligosaccharides based on the evaluation of oligomer ratios using an artificial neural network

The application of an internal standard in quantitative analysis is desirable in order to correct for variations in sample preparation and instrumental response. In mass spectrometry of organic compounds, the internal standard is preferably labelled with a stable isotope, such as ¹⁸O, ¹⁵N or ¹³C. In this study, a method for the quantification of fructo-oligosaccharides using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI TOF MS) was proposed and tested on raftilose, a partially hydrolysed inulin with a degree of polymeration 2-7. A tetraoligosaccharide nystose, which is chemically identical to the raftilose tetramer, was used as an internal standard rather than an isotope-labelled analyte. Two mathematical approaches used for data processing, conventional calculations and artificial neural networks (ANN), were compared. The conventional data processing relies on the assumption that a constant oligomer dispersion profile will change after the addition of the internal standard and some simple numerical calculations. On the other hand, ANN was found to compensate for a non-linear MALDI response and variations in the oligomer dispersion profile with raftilose concentration. As a result, the application of ANN led to lower quantification errors and excellent day-to-day repeatability compared to the conventional data analysis. The developed method is feasible for MS quantification of raftilose in the range of 10-750 pg with errors below 7%. The content of raftilose was determined in dietary cream; application can be extended to other similar polymers. It should be stressed that no special optimisation of the MALDI process was carried out. A common MALDI matrix and sample preparation were used and only the basic parameters, such as sampling and laser energy, were optimised prior to quantification.     

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.     

Selection of quasi-optimal inputs in chemometrics modeling by artificial neural network analysis

Instrumentation spectra used for chemometrics analysis are often too unwieldy to model, as many of the inputs do not contain important information. Several mathematical methods are used for reducing the number of inputs to the significant ones only. Artificial neural networks (ANN) modeling suffers from difficulties in training models with a large number of inputs. However, using a non-random initial connection weight algorithm and local minima avoidance and escape techniques can overcome these difficulties. Once the ANN model is trained, the analysis of its connection weights can easily identify the more relevant inputs. Repeating the process of training the ANN model with the reduced input set and the selection of the more relevant inputs can proceed until a quasi-optimal, small, set of inputs is identified. Two examples are presented—finding the minimal set of wavelengths in benchmark diesel fuel NIR spectra, and in spectra generated in a recent work, modeling of “artificial nose” sensor array. In the last example, 1260 inputs were reduced to optimal sets of <10 inputs. Causal index calculation can analyze the influence of each of selected wavelengths on the predicted property. Some of the resulting minimal sets are not unique, depending on the ANN architecture used in the training. The accuracy of the resulting ANN models is usually better, and more robust, than the original large ANN model.     

Prediction dielectric constant of different ternary liquid mixtures at various temperatures and compositions using artificial neural networks

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction dielectric constant of different ternary liquid mixtures at various temperatures (?10°C?≤?t?≤?80°C) and over the complete composition range (0?≤?x ₁,?x ₂,?x ₃?≤?1). A three-layered feed forward ANN with architecture 7-16-1 was generated using seven parameters as inputs and its output is dielectric constant of media. It was found that properly selected and trained neural network could fairly represent the dependence of dielectric constant of different ternary liquid mixtures on temperature and composition. For the evaluation of the predictive power of the generated ANN, an optimized network was applied for predicting the dielectric constant in the prediction set, which were not used in the modeling procedure. Squared correlation coefficient (R ²) and root mean square error for prediction set are 0.9997 and 0.2060, respectively. The mean percent deviation (MPD) for the property in the prediction set is 0.8892%. The results show nonlinear dependence of dielectric constant of ternary mixed solvent systems on temperature and composition is significant.     

The SAAP force field. A simple approach to a new all-atom protein force field by using single amino acid potential (SAAP) functions in various solvents

A simple strategy to compose a new all-atom protein force field (named as the SAAP force field), which utilizes the single amino acid potential (SAAP) functions obtained in various solvents by ab initio molecular orbital calculation applying the isodensity polarizable continuum model (IPCM), is presented. We considered that the total energy function of a protein force field (E(TOTAL)) is divided into three components; a single amino acid potential term (E(SAAP)), an interamino acid nonbonded interaction term (E(INTER)), and a miscellaneous term (E(OTHERS)), which is ignored (or considered to be constant) at the current version of the force field. The E(INTER) term consists of electrostatic interactions (E(ES')) and van der Waals interactions (E(LJ')). Despite simplicity, the SAAP force field implicitly involves the correlation among individual terms of the Lifson's potential function within a single amino acid unit and can treat solvent effects unambiguously by choosing the SAAP function in an appropriate solvent and the dielectric constant (D) of medium. Application of the SAAP force field to the Monte Carlo simulation of For-Ala(2)-NH(2) in vacuo reasonably reproduced the results of the extensive conformational search by ab initio molecular orbital calculation. In addition, the preliminary Monte Carlo simulations for For-Gly(10)-NH(2) and For-Ala(10)-NH(2) showed reversible transitions from the extended to the pseudosecondary structures in water (D = 78.39) as well as in ether (D = 4.335). The result suggested that the new approach is efficient for fast modeling of protein structures in various environments. Decomposition analysis of the total energy function (E(TOTAL)) by using the SAAP force field suggested that conformational propensities of single amino acids (i.e., the E(SAAP) term) may play definitive roles on the topologies of protein secondary structures.     

Analysis of ultrasonic velocity in refrigerants using artificial neural network

Several researchers have reported numerous measurements on ultrasonic velocity as a function of temperature and pressure using various experimental techniques. A large amount of experimental data is required in order to obtain accurate results for the chemical substances used. The present article explores the evaluation of ultrasonic velocity as a function of molecular weight, temperature and pressure using an artificial neural network (ANN) in six refrigerants. The network so developed predicts the ultrasonic velocity successfully. Statistical analysis of the results was performed using standard deviation (%) and relative average deviation. The correlation coefficient in our analysis was found to be 0.9999. The trained weights, obtained from ANN, are further employed to form equations to predict ultrasonic velocity at other temperatures and pressures.     

Efficiency of nested Markov chain Monte Carlo for polarizable potentials and perturbed Hamiltonians

Nested Markov chain Monte Carlo is a rigorous way to enhance sampling of a given energy landscape using an auxiliary, approximate potential energy surface. Its practical efficiency mainly depends on how cheap and how different are the auxiliary potential with respect to the reference system. In this article, a combined efficiency index is proposed and assessed for two important families of energy surfaces. As illustrated for water clusters, many‐body polarizable potentials can be approximated by simplifying the polarization contribution and keeping only the two‐body terms. In small systems, neglecting polarization entirely is also acceptable. When the reference potential energy is obtained from diagonalization of a quantum mechanical Hamiltonian, a first‐order perturbation scheme can be used to estimate the energy difference occuring on a Monte Carlo move. Our results indicate that this perturbation approximation performs well provided that the number of steps between successive diagonalization is adjusted beforehand. © 2010 Wiley Periodicals, Inc. Int J Quantum Chem 110:2342–2346, 2010     

人工神经网络法同时测定苯二酚异构体

研究了人工神经网络法与紫外分光光度分析相结合，用于邻苯二酚、间苯二酚、对苯二酚异构体的含量测定。本研究采用人工神经网络法，直接对苯二酚异构体混合液的紫外吸收光谱数据进行预测，不需预先分离，即可得到各异构体的浓度。     

Non-destructive determination of metronidazole powder by using artificial neural networks on short-wavelength NIR spectroscopy

The present study aimed at providing a new method in sight into short-wavelength near-infrared (NIR) spectroscopy of in pharmaceutical quantitative analysis. To do that, 124 experimental samples of metronidazole powder were analyzed using artificial neural networks (ANNs) in the 780-1100 nm region of short-wavelength NIR spectra. In this paper, metronidazole was as active component and other two components (magnesium stearate and starch) were as excipients. Different preprocessing spectral data (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) were applied to establish the ANNs models of metronidazole powder. The degree of approximation, a new evaluation criterion of the networks was employed to prove the accuracy of the predicted results. The results presented here demonstrate that the short-wavelength NIR region is promising for the fast and reliable determination of major component in pharmaceutical analysis.     

Forecasting human exposure to PM10 at the national level using an artificial neural network approach

A neural network model for predicting country‐level concentrations of the fraction of particulates in the air with sizes less than 10 µm (PM10) has been developed using widely available sustainability and economical/industrial parameters as inputs. The model was trained and validated with the data for 23 European Union (EU) countries plus the EU27 as a group for the period from 2000 to 2008. The inputs for the model were selected using correlation analyses. Country‐level PM10 concentration data that were used as a model output were obtained from the World Bank. The artificial neural network (ANN) model, created with inputs chosen by correlation analyses, has shown very good performance in the forecast of country‐level PM10 concentrations. The mean absolute error for the ANN model prediction, in the case of most of the EU countries, was less than 13%, indicating stable and accurate predictions. The predictions obtained from the principal component regression model, which was trained and tested using the same datasets and input variables, had mean absolute errors from 20% to 150% for most of the countries. The wide availability of input parameters used in this model can overcome the problem of lack and scarcity of data in many countries, which can in turn prevent the determination of human exposure to PM10 at the national level. Copyright © 2013 John Wiley & Sons, Ltd.     

Catechol determination in compost bioremediation using a laccase sensor and artificial neural networks

An electrochemical biosensor based on the immobilization of laccase on magnetic core-shell (Fe₃O₄–SiO₂) nanoparticles was combined with artificial neural networks (ANNs) for the determination of catechol concentration in compost bioremediation of municipal solid waste. The immobilization matrix provided a good microenvironment for retaining laccase bioactivity, and the combination with ANNs offered a good chemometric tool for data analysis in respect to the dynamic, nonlinear, and uncertain characteristics of the complex composting system. Catechol concentrations in compost samples were determined by using both the laccase sensor and HPLC for calibration. The detection range varied from 7.5 × 10^–7 to 4.4 × 10^–4 M, and the amperometric response current reached 95% of the steady-state current within about 70 s. The performance of the ANN model was compared with the linear regression model in respect to simulation accuracy, adaptability to uncertainty, etc. All the results showed that the combination of amperometric enzyme sensor and artificial neural networks was a rapid, sensitive, and robust method in the quantitative study of the composting system. Figure Structure of the magnetic carbon paste electrode used in the electrochemical biosensor     

Single-step calibration, prediction and real samples data acquisition for artificial neural network using a CCD camera

An artificial neural network (ANN) model is developed for simultaneous determination of Al(III) and Fe(III) in alloys by using chrome azurol S (CAS) as the chromogenic reagent and CCD camera as the detection system. All calibration, prediction and real samples data were obtained by taking a single image. Experimental conditions were established to reduce interferences and increase sensitivity and selectivity in the analysis of Al(III) and Fe(III). In this way, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Sigmoid transfer functions were used in the hidden and output layers to facilitate nonlinear calibration. Both Al(III) and Fe(III) can be determined in the concentration range of 0.25-4 μg ml⁻¹ with satisfactory accuracy and precision. The proposed method was also applied satisfactorily to the determination of considered metal ions in two synthetic alloys.     

Ab initio parameterization of an all-atom polarizable and dissociable force field for water

A novel all-atom, dissociative, and polarizable force field for water is presented. The force field is parameterized based on forces, stresses, and energies obtained form ab initio calculations of liquid water at ambient conditions. The accuracy of the force field is tested by calculating structural and dynamical properties of liquid water and the energetics of small water clusters. The transferability of the force field to dissociated states is studied by considering the solvation of a proton and the ionization of water at extreme conditions of pressure and temperature. In the case of the solvated proton, the force field properly describes the presence of both Eigen and Zundel configurations. In the case of the pressure-induced ice VIII/ice X transition and the temperature-induced transition to a superionic phase, the force field is found to describe accurately the proton symmetrization and the melting of the proton sublattice, respectively.     

An experimental design approach employing artificial neural networks for the determination of potential endocrine disruptors in food using matrix solid-phase dispersion

Matrix solid-phase dispersion (MSPD) as a sample preparation method for the determination of two potential endocrine disruptors, linuron and diuron and their common metabolites, 1-(3,4-dichlorophenyl)-3-methylurea (DCPMU), 1-(3,4-dichlorophenyl) urea (DCPU) and 3,4-dichloroaniline (3,4-DCA) in food commodities has been developed. The influence of the main factors on the extraction process yield was thoroughly evaluated. For that purpose, a 3^(4–1) fractional factorial design in further combination with artificial neural networks (ANNs) was employed. The optimal networks found were afterwards used to identify the optimum region corresponding to the highest average recovery displaying at the same time the lowest standard deviation for all analytes. Under final optimal conditions, potato samples (0.5 g) were mixed and dispersed on the same amount of Florisil. The blend was transferred on a polypropylene cartridge and analytes were eluted using 10 ml of methanol. The extract was concentrated to 50 μl of acetonitrile/water (50:50) and injected in a high performance liquid chromatography coupled to UV–diode array detector system (HPLC/UV–DAD). Recoveries ranging from 55 to 96% and quantification limits between 5.3 and 15.2 ng/g were achieved. The method was also applied to other selected food commodities such as apple, carrot, cereals/wheat flour and orange juice demonstrating very good overall performance.     

Correlation of solubility data of ammonia in ionic liquids for gas separation processes using artificial neural networks

Artificial neural networks have been used for the correlation and prediction of solubility data of ammonia in ionic liquids. This solubility of ammonia is highly variable for different types of ionic liquids at the same temperature and pressure, its correlation and prediction is of special importance in the removal of ammonia from flue gases for which effective and efficient solvents are required. Nine binary ammonia + ionic liquids mixtures were considered in the study. Solubility data (P–T–x) of these systems were taken from the literature (208 data points for training and 50 data points for testing). The training variables are the temperature and the pressure of the binary systems (T, P), being the target variable the solubility of ammonia in the ionic liquid (x). The study shows that the neural network model is a good alternative method for the estimation of solubility for this type of mixtures. Absolute average deviations were below 5.6%, for each isothermal data set and overall absolute average deviations were below 3.0%. Only in the range of low solubility (below 0.2 in mole fraction) did predicted solubility give deviations higher than 10%.     

Simultaneous determination of three organophosphorus pesticides residues in vegetables using continuous-flow chemiluminescence with artificial neural network calibration

In this article, a continuous-flow chemiluminescence (CL) system with artificial neural network calibration is proposed for simultaneous determination of three organophosphorus pesiticides residues. This method is based on the fact that organophosphorus pesticides can be decomposed into orthophosphate with potassium peroxodisulphate as oxidant under ultraviolet radiation and that the decomposing kinetic characteristics of the organophosphorus pesticides with different molecular structure are significantly different. The produced orthophosphate can react with molybdate and vanadate to form the vanadomolybdophosphoric heteropoly acid, which can oxidize luminol to produce intense CL emission. The CL intensity of the solution was measured and recorded every 2 s in the range of 0-250 s. The obtained data were processed chemometrically by use of a three-layered feed-forward artificial neural network trained by back-propagation learning algorithm, in which input node, hidden node and output nodes were 65, 21 and 3, respectively. The proposed multi-residue analysis method was successfully applied to the simultaneous determination of the three organophosphorus pesticides residue in some vegetables samples.