Distribution by Scientific Domains
Distribution within Chemistry

Terms modified by NN

  • nn interval
  • nn model

  • Selected Abstracts

    A web-based tool for teaching neural network concepts

    Aybars Ugur
    Abstract Although neural networks (NN) are important especially for engineers, scientists, mathematicians and statisticians, they may also be hard to understand. In this article, application areas of NN are discussed, basic NN components are described and it is explained how an NN work. A web-based simulation and visualization tool (EasyLearnNN) is developed using Java and Java 2D for teaching NN concepts. Perceptron, ADALINE, Multilayer Perceptron, LVQ and SOM models and related training algorithms are implemented. As a result, comparison with other teaching methods of NN concepts is presented and discussed. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 449,457, 2010; View this article online at wileyonlinelibrary.com; DOI 10.1002/cae.20184 [source]

    A design-variable-based inelastic hysteretic model for beam,column connections

    Gun Jin Yun
    Abstract This paper presents a design-variable-based inelastic hysteretic model for beam,column connections. It has been well known that the load-carrying capacity of connections heavily depends on the types and design variables even in the same connection type. Although many hysteretic connection models have been proposed, most of them are dependent on the specific connection type with presumed failure mechanisms. The proposed model can be responsive to variations both in design choices and in loading conditions. The proposed model consists of two modules: physical-principle-based module and neural network (NN)-based module in which information flow from design space to response space is formulated in one complete model. Moreover, owing to robust learning capability of a new NN-based module, the model can also learn complex dynamic evolutions in response space under earthquake loading conditions, such as yielding, post-buckling and tearing, etc. Performance of the proposed model has been demonstrated with synthetic and experimental data of two connection types: extended-end-plate and top- and seat-angle with double-web-angle connection. Furthermore, the design-variable-based model can be customized to any structural component beyond the application to beam,column connections. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Dehydrogenation of Hydridoirida-,-diketones in Methanol: The Selective Formation of Mono- and Dinuclear Acyl Complexes

    Roberto Ciganda
    Abstract The hydridoirida-,-diketone [IrH{(PPh2(o -C6H4CO))2H}Cl] (1) reacts with diimines (NN) or with pyridine (py) in refluxing methanol to undergo dehydrogenation. The reactions afford selectively the cis -acyl, trans -phosphane isomers of the cationic [Ir(PPh2(o -C6H4CO))2(NN)]+ {NN = 2,2,-bipyridine (2); R,N=C(CH3),C(CH3)=N,R, [R = R, = NH2 (3); R = R, = OH (4); R = OH, R, = NH2 (5)]} or neutral [IrCl(PPh2(o -C6H4CO))2(py)] (6) derivatives. The reactions are faster for ligands containing amino substituents. Refluxing 1 in MeOH affords the formation of an equimolar mixture of dimercationic species [Ir2(,-Cl)(,-PPh2(o -C6H4CO))2(PPh2(o -C6H4CO))2]+ (7a and 7b) containing two acyls and a chloride as bridging groups. The isomers could be separated by fractional precipitation. Compound [3]Cl, containing amino substituents in the imino functionalities, catalyses the hydrogen transfer from 2-propanol to cyclohexanone to afford cyclohexanol. All the complexes were fully characterised spectroscopically. Single crystal X-ray diffraction analysis was performed on complexes 6 and [7b]ClO4. [source]

    Synthesis and Photophysical Properties of Copper(I) Complexes Obtained from 1,10-Phenanthroline Ligands with Increasingly Bulky 2,9-Substituents

    Gianluca Accorsi
    Abstract In this paper, we describe the synthesis and the electronic properties of a series of [Cu(NN)2]+ systems. The NN ligands investigated are 2,9-bis[(tert -butyldimethylsilyloxy)methyl]-1,10-phenanthroline (1), 2,9-bis[(triisopropylsilyloxy)methyl]-1,10-phenanthroline (2), 2,9-bis[(tert -butyldiphenylsilylmoxy)ethyl]-1,10-phenanthroline (3), 2,9-bis[2,6-bis(benzyloxy)phenethyl]-1,10-phenanthroline (4) and 2-(1,3-diphenylpropan-2-yl)-9-phenethyl-1,10-phenanthroline (5). The electrochemical properties and the ground state electronic absorption spectra of Cu(1)2,Cu(5)2 are in line with the classical behaviour of such [Cu(NN)2]+ derivatives. Whereas all the compounds exhibit MLCT luminescence centered around 630,650 nm, the emission quantum yields and the lifetimes are dramatically different as a function of stereoelectronic effects and/or the possibility of internal exciplex quenching when oxygen-containing functional groups are attached to the phenanthroline ligands. [source]

    Uncommon Solvent Effect in Oxidative Addition of MeI to a New Dinuclear Platinum Complex Containing a Platina(II)cyclopentane Moiety

    S. Jafar Hoseini
    Abstract The reaction of the known complex cis,cis -[Me2Pta(,-dppm)(,-SMe2)Ptb,CH2(CH2)2CcH2(Ptb,Cc)] [dppm = bis(diphenylphosphanyl)methane] with phthalazine (NN) proceeded by replacement of the labile bridging SMe2 ligand with the bidentate N-donor ligand NN to give cis,cis -[Me2Pta(,-dppm)(,-NN)Ptb,CH2(CH2)2CcH2(Ptb,Cc)] (1) as a pale red solid in good yield. The complex was fully characterized by multinuclear (1H, 31P, 195Pt) NMR spectroscopy. The subsequent reaction of complex 1 with excess MeI gave the colorless diplatinum(IV) complex [Me3Pta(,-dppm)(,-I)2Ptb{CH2(CH2)2CcH2(Ptb,Cc)}Me], in which the bridging NN ligand is replaced by bridging iodido ligands. The reddish color of complex 1, which is due to a metal-to-ligand charge transfer (MLCT) band in the visible region, was used to monitor its reaction with MeI in the solvents acetone, CH2Cl2, and benzene. The kinetic data revealed that the reactions in nonpolar benzene or slightly polar CH2Cl2 proceeded in two steps, each following a common SN2 mechanism. In the first step, MeI attacked the platina(II)cyclopentane center rather than the dimethylplatinum(II) center, because the first center is more electron-rich than the second center. In the more polar acetone, the reaction proceeded similarly, with the exception that each step was accompanied by a solvolytic reaction, which was suggested to be responsible for the unusually slower reaction rate in acetone than in benzene or CH2Cl2. Consistently, the reaction rate in the highly polar solvent CH3CN was too slow for any meaningful measurement.(© Wiley-VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2008) [source]

    The application of NN technique to automatic generation control for the power system with three areas including smes units

    A. Demirören
    The study includes an application of layered neural network controller to study automatic generation control (AGC) problem of the power system, which contains superconducting magnetic energy storage (SMES) units. The effectiveness of SMES unit over frequency oscillations improvement against load perturbations in power system is well known. In addition, the proposed control scheme provides the steady state error of frequency and inadvertent interchange of tie-lines to be maintained in steady state values. The power system considered has three areas two of which including steam turbines while the other containing a hydro turbine, and all of them contain SMES units, in addition. In the power system each area with a steam turbine contains the non-linearity due to reheat effect of the steam turbine and all of the areas contain upper and lower constraints for generation rate. Only one neural network (NN) controller, which controls all the inputs of each area in the power system, is considered. In the NN controller, back propagation-through-time algorithm is used as neural network learning rule. The performance of the power system is simulated by using conventional integral controller and NN controller for the cases with or without SMES units in all areas, separately. By comparing the results for both cases, it can be seen that the performance of NN controller is better than conventional controllers. [source]

    Load frequency control for power system with reheat steam turbine and governor deadband non-linearity by using neural network controller

    H. L. Zeynelgil
    In this paper, a neural network (NN) controller is presented for load-frequency control of power system. The NN controller uses back propagation-through-time algorithm. In the power system, the reheat effect of the steam turbine and the effect of governor deadband non-linearity are considered by describing function approach in the state space model. By comparing the results of simulations, the performance of the NN controller is better than conventional controller. NN controller gives a shorter settling time and eliminates the necessity of parameter estimation time required in conventional adaptive control techniques. [source]

    Shared near neighbours neural network model: a debris flow warning system

    Fi-John Chang
    Abstract The main purpose of this study is to develop a new type of artificial neural network based model for constructing a debris flow warning system. The Chen-Eu-Lan river basin, which is located in Central Taiwan, is assigned as the study area. The creek is one of the most well-known debris flow areas where several damaging debris flows have been reported in the last two decades. The hydrological and geological data, which might have great influence on the occurrence of debris flows, are first collected and analysed, then, the shared near neighbours neural network (SNN + NN) is presented to construct the debris flow warning system for the watershed. SNN is an unsupervised learning method that has the advantage of dealing with non-globular clusters, besides presenting computational efficiency. By using SNN, the compiled hydro-geological data set can easily and meaningfully be clustered into several categories. These categories can then be identified as ,occurrence' or ,no-occurrence' of debris flows. To improve the effectiveness of the debris flow warning system, a neural network framework is designed to connect all the clusters produced by the SNN method, whereas the connected weights of the network are adjusted through a supervised learning method. This framework is used and its applicability and practicability for debris flow warning are investigated. The results demonstrate that the proposed SNN + NN model is an efficient and accurate tool for the development of a debris flow warning system. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Applications of Sinusoidal Neural Network and Momentum Genetic Algorithm to Two-wheel Vehicle Regulating Problem

    Duong Chau Sam Non-member
    Abstract In an attempt to enhance the performance of neural network (NN), we propose a sinusoidal activation function for NN and apply a fast genetic algorithm (GA) with uses of momentum offspring (MOS) and constant-range mutation (CRM) for training the NN. The proposed methods are aimed at designing a neurocontroller (NC) for regulating a two-wheel vehicle system, known as nonholonomic system, in the viewpoint that it is necessary to improve the control process of the system even though several control methods, including applications of NN and GAs, have been developed. The learning performances of NCs are evaluated through the successful evolutionary rates of the control process based on the values of the squared errors. In order to compare the conventional methods with our proposed approaches and verify the effects of momentum GA on NC training, various numerical simulations will be carried out with different numbers of generations in GAs and different activation functions of NCs. Finally, the controllability of NC is investigated with certain sets of initial states. The simulations show that sinusoidal NC trained by momentum GA has a good performance regardless of the small values of population size and generations in GA. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source]

    A comparison of nearest neighbours, discriminant and logit models for auditing decisions

    Chrysovalantis Gaganis
    This study investigates the efficiency of k -nearest neighbours (k -NN) in developing models for estimating auditors' opinions, as opposed to models developed with discriminant and logit analyses. The sample consists of 5276 financial statements, out of which 980 received a qualified audit opinion, obtained from 1455 private and public UK companies operating in the manufacturing and trade sectors. We develop two industry-specific models and a general one using data from the period 1998,2001, which are then tested over the period 2002,2003. In each case, two versions of the models are developed. The first includes only financial variables. The second includes both financial and non-financial variables. The results indicate that the inclusion of credit rating in the models results in a considerable increase both in terms of goodness of fit and classification accuracies. The comparison of the methods reveals that the k -NN models can be more efficient, in terms of average classification accuracy, than the discriminant and logit models. Finally, the results are mixed concerning the development of industry-specific models, as opposed to general models. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Generalized strain probing of constitutive models

    Youssef M. A. Hashash
    Abstract Advanced material constitutive models are used to describe complex soil behaviour. These models are often used in the solution of boundary value problems under general loading conditions. Users and developers of constitutive models need to methodically investigate the represented soil response under a wide range of loading conditions. This paper presents a systematic procedure for probing constitutive models. A general incremental strain probe, 6D hyperspherical strain probe (HSP), is introduced to examine rate-independent model response under all possible strain loading conditions. Two special cases of HSP, the true triaxial strain probe (TTSP) and the plane-strain strain probe (PSSP), are used to generate 3-D objects that represent model stress response to probing. The TTSP, PSSP and general HSP procedures are demonstrated using elasto-plastic models. The objects resulting from the probing procedure readily highlight important model characteristics including anisotropy, yielding, hardening, softening and failure. The PSSP procedure is applied to a Neural Network (NN) based constitutive model. It shows that this probing is especially useful in understanding NN constitutive models, which do not contain explicit functions for yield surface, hardening, or anisotropy. Copyright © 2004 John Wiley & Sons, Ltd. [source]

    Neural network-based adaptive control of piezoelectric actuators with unknown hysteresis

    Wen-Fang Xie
    Abstract This paper proposes a neural network (NN)-based adaptive control of piezoelectric actuators with unknown hysteresis. Based on the classical Duhem model described by a differential equation, the explicit solution to the equation is explored and a new hysteresis model is constructed as a linear model in series with a piecewise continuous nonlinear function. An NN-based dynamic pre-inversion compensator is designed to cancel out the effect of the hysteresis. With the incorporation of the pre-inversion compensator, an adaptive control scheme is proposed to have the position of the piezoelectric actuator track the desired trajectory. This paper has three distinct features. First, it applies the NN to online approximate complicated piecewise continuous unknown nonlinear functions in the explicit solution to Duhem model. Second, an observer is designed to estimate the output of hysteresis of piezoelectric actuator based on the system input and output. Third, the stability of the controlled piezoelectric actuator with the observer is guaranteed. Simulation results for a practical system validate the effectiveness of the proposed method in this paper. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Adaptive critic design using non-linear network structures

    Ognjen Kuljaca
    Abstract A neural net (NN)/fuzzy logic (FL) adaptive critic controller is described. This structure takes advantage of the decision-making properties of a FL system to critique and tune a NN action-generating network. The stability of the proposed structure is proven. NN and fuzzy weight tuning algorithms are given that do not require complicated initialization procedures or any off-line learning phase. Tracking and bounded NN weights and control signals are guaranteed. The adaptive fuzzy critic controller given here is a model-free controller' in the sense that it works for any system in a prescribed class without the need for extensive modeling and preliminary analysis to find a regression matrix'. There is no linearity in the parameter (LIP) requirement. Copyright © 2003 John Wiley & Sons, Ltd. [source]

    Identification of a class of non-linear parametrically varying models

    F. Previdi
    The aim of this paper is to propose a novel class of non-linear, possibly parameter-varying models suitable for system identification purposes. These models are given in the form of a linear fractional transformation (LFT) where the ,forward' part is represented by a conventional linear regression and the ,feedback' part is given by a non-linear dynamic map parameterized by a neural network (NN) which can take into account scheduling variables available for measurement. For this specific model structure a parameter estimation procedure has been set up, which turns out to be particularly efficient from the computational point of view. Also, it is possible to establish a connection between this model class and the well known class of local model networks (LMNs): this aspect is investigated in the paper. Finally, we have applied the proposed identification procedure to the problem of determining accurate non-linear models for knee joint dynamics in paraplegic patients, within the framework of a functional electrical stimulation (FES) rehabilitation engineering project. Copyright © 2002 John Wiley & Sons, Ltd. [source]

    Oral health status and treatment needs among school children in Sana'a City, Yemen

    KA Al-Haddad
    To cite this article: Int J Dent Hygiene DOI: 10.1111/j.1601-5037.2009.00398.x Al-Haddad KA, Al-Hebshi NN, Al-Ak'hali MS. Oral health status and treatment needs among school children in Sana'a City, Yemen. Abstract:, Data on the oral health status and treatment needs among Yemeni children are lacking. Objectives:, To assess caries prevalence, treatment needs and gingival health status among school children in Sana'a City and to examine how these are affected by age, gender and khat chewing. Methods:, 1489 children (6- to 14-year old) were randomly selected from 27 schools representing all nine districts of Sana'a City. Dental caries and treatment needs were evaluated using standard WHO oral survey methods. The plaque index (PI), calculus index (CI) and the gingival index (GI), recorded at the six Ramfjord's teeth, were used to assess gingival health status. Results:, 4.1% of the study subjects were caries-free. Prevalence of these was significantly higher among the males. Overall, mean dmfs, dmft, DMFS and DMFT scores were 8.45, 4.16, 3.59 and 2.25 respectively. The decayed component accounted for >85% of the scores. The highest dmfs/dmft means were found among the 6,8 years age group, while the highest DMFS/DMFT means were scored by the 12,14 years age group. The need for restorative treatment and extractions was high; the former was significantly higher among the females. All subjects had gingivitis; the mean PI, CI and GI were 1.25, 0.3 and 1.36 respectively. Khat chewing did not affect caries experience; however, it was significantly associated with higher PI, CI and GI scores. Conclusions:, The prevalence of caries, gingivitis and treatment needs among children in Sana'a city is high. More surveys in other Yemeni cities to generate comprehensive data are required. [source]

    Sensitivity analysis of neural network parameters to improve the performance of electricity price forecasting

    Paras Mandal
    Abstract This paper presents a sensitivity analysis of neural network (NN) parameters to improve the performance of electricity price forecasting. The presented work is an extended version of previous works done by authors to integrate NN and similar days (SD) method for predicting electricity prices. Focus here is on sensitivity analysis of NN parameters while keeping the parameters same for SD to forecast day-ahead electricity prices in the PJM market. Sensitivity analysis of NN parameters include back-propagation learning set (BP-set), learning rate (,), momentum (,) and NN learning days (dNN). The SD parameters, i.e. time framework of SD (d=45 days) and number of selected similar price days (N=5) are kept constant for all the simulated cases. Forecasting performance is carried out by choosing two different days from each season of the year 2006 and for which, the NN parameters for the base case are considered as BP-set=500, ,=0.8, ,=0.1 and dNN=45 days. Sensitivity analysis has been carried out by changing the value of BP-set (500, 1000, 1500); , (0.6, 0.8, 1.0, 1.2), , (0.1, 0.2, 0.3) and dNN (15, 30, 45 and 60 days). The most favorable value of BP-set is first found out from the sensitivity analysis followed by that of , and ,, and based on which the best value of dNN is determined. Sensitivity analysis results demonstrate that the best value of mean absolute percentage error (MAPE) is obtained when BP-set=500, ,=0.8, ,=0.1 and dNN=60 days for winter season. For spring, summer and autumn, these values are 500, 0.6, 0.1 and 45 days, respectively. MAPE, forecast mean square error and mean absolute error of reasonably small value are obtained for the PJM data, which has correlation coefficient of determination (R2) of 0.7758 between load and electricity price. Numerical results show that forecasts generated by developed NN model based on the most favorable case are accurate and efficient. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Assessment of four modifications of a novel indexing technique for case-based reasoning

    Mykola Galushka
    In this article, we investigate four variations (D-HSM, D-HSW, D-HSE, and D-HSEW) of a novel indexing technique called D-HS designed for use in case-based reasoning (CBR) systems. All D-HS modifications are based on a matrix of cases indexed by their discretized attribute values. The main differences between them are in their attribute discretization stratagem and similarity determination metric. D-HSM uses a fixed number of intervals and simple intersection as a similarity metric; D-HSW uses the same discretization approach and a weighted intersection; D-HSE uses information gain to define the intervals and simple intersection as similarity metric; D-HSEW is a combination of D-HSE and D-HSW. Benefits of using D-HS include ease of case and similarity knowledge maintenance, simplicity, accuracy, and speed in comparison to conventional approaches widely used in CBR. We present results from the analysis of 20 case bases for classification problems and 15 case bases for regression problems. We demonstrate the improvements in accuracy and/or efficiency of each D-HS modification in comparison to traditional k -NN, R-tree, C4,5, and M5 techniques and show it to be a very attractive approach for indexing case bases. We also illuminate potential areas for further improvement of the D-HS approach. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 353,383, 2007. [source]

    Flexible models with evolving structure

    Plamen P. Angelov
    A type of flexible model in the form of a neural network (NN) with evolving structure is discussed in this study. We refer to models with amorphous structure as flexible models. There is a close link between different types of flexible models: fuzzy models, fuzzy NN, and general regression models. All of them are proven universal approximators and some of them [Takagi-Sugeno fuzzy model with singleton outputs and radial-basis function] are interchangeable. The evolving NN (eNN) considered here makes use of the recently introduced on-line approach to identification of Takagi-Sugeno fuzzy models with evolving structure (eTS). Both TS and eNN differ from the other model schemes by their gradually evolving structure as opposed to the fixed structure models, in which only parameters are subject to optimization or adaptation. The learning algorithm is incremental and combines unsupervised on-line recursive clustering and supervised recursive on-line output parameter estimation. eNN has potential in modeling, control (if combined with the indirect learning mechanism), fault detection and diagnostics etc. Its computational efficiency is based on the noniterative and recursive procedure, which combines the Kalman filter with proper initializations and on-line unsupervised clustering. The eNN has been tested with data from a real air-conditioning installation. Applications to real-time adaptive nonlinear control, fault detection and diagnostics, performance analysis, time-series forecasting, knowledge extraction and accumulation, are possible directions of their use in future research. © 2004 Wiley Periodicals, Inc. [source]

    A comparative study on a novel model-based PID tuning and control mechanism for nonlinear systems

    S. Iplikci
    Abstract This work presents a novel predictive model-based proportional integral derivative (PID) tuning and control approach for unknown nonlinear systems. For this purpose, an NARX model of the plant to be controlled is obtained and then it used for both PID tuning and correction of the control action. In this study, for comparison, neural networks (NNs) and support vector machines (SVMs) have been used for modeling. The proposed structure has been tested on two highly nonlinear systems via simulations by comparing control and convergence performances of SVM- and NN-Based PID controllers. The simulation results have shown that when used in the proposed scheme, both NN and SVM approaches provide rapid parameter convergence and considerably high control performance by yielding very small transient- and steady-state tracking errors. Moreover, they can maintain their control performances under noisy conditions, while convergence properties are deteriorated to some extent due to the measurement noises. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    Adaptive backstepping control for a class of nonlinear systems using neural network approximations

    K. K. Tan
    In this paper, an adaptive neural network (NN) backstepping technique is developed for tracking control of a class of nonlinear systems. NNs are used to compensate for the unknown nonlinear functions in the system. A systematic backstepping approach is established to synthesize the adaptive NN control scheme that ensures the boundedness of all the signals in the closed-loop system, and yields a small tracking error. The issue of transient performance is also addressed under an analytical framework. The effectiveness of the proposed scheme is demonstrated by computer simulations. Copyright © 2004 John Wiley & Sons, Ltd. [source]

    Desensitizing models using covariance matrix transforms or counter-balanced distortions

    Rocco DiFoggio
    Abstract This paper presents a generalization of the Lagrange multiplier equation for a regression subject to constraints. It introduces two methods for desensitizing models to anticipated spectral artifacts such as baseline variations, wavelength shift, or trace contaminants. For models derived from a covariance matrix such as multiple linear regression (MLR) and principal components regression (PCR) models, the first method shows how a covariance matrix can be desensitized to an artifact spectrum, v, by adding ,2v,,,v to it. For models not derived from a covariance matrix, such as partial least squares (PLS) or neural network (NN) models, the second method shows how distorted copies of the original spectra can be prepared in a counter-balanced manner to achieve desensitization. Unlike earlier methods that added random distortions to spectra, these new methods never introduce any accidental correlations between the added distortions and the Y -block. The degree of desensitization is controlled by a parameter, ,, for each artifact from zero (no desensitization) to infinity (complete desensitization, which is the Lagrange multiplier limit). Unlike Lagrange multipliers, these methods permit partial desensitization so we can individually vary the degree of desensitization to each artifact, which is important when desensitization to one artifact inhibits desensitization to another. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    Intramolecular electronic communication in a dimethylaminoazobenzene,fullerene C60 dyad: An experimental and TD-DFT study

    K. Senthil Kumar
    Abstract An electronically push,pull type dimethylaminoazobenzene,fullerene C60 hybrid was designed and synthesized by tailoring N,N -dimethylaniline as an electron donating auxochrome that intensified charge density on the ,-azonitrogen, and on N -methylfulleropyrrolidine (NMFP) as an electron acceptor at the 4 and 4, positions of the azobenzene moiety, respectively. The absorption and charge transfer behavior of the hybrid donor-bridge-acceptor dyad were studied experimentally and by performing TD-DFT calculations. The TD-DFT predicted charge transfer interactions of the dyad ranging from 747 to 601 nm were experimentally observed in the UV-vis spectra at 721 nm in toluene and dichloromethane. A 149 mV anodic shift in the first reduction potential of the NN group of the dyad in comparison with the model aminoazobenzene derivative further supported the phenomenon. Analysis of the charge transfer band through the orbital picture revealed charge displacement from the n(NN) (nonbonding) and , (NN) type orbitals centered on the donor part to the purely fullerene centered LUMOs and LUMO+n orbitals, delocalized over the entire molecule. The imposed electronic perturbations on the aminoazobenzene moiety upon coupling it with C60 were analyzed by comparing the TD-DFT predicted and experimentally observed electronic transition energies of the dyad with the model compounds, NMFP and (E)-N,N -dimethyl-4-(p-tolyldiazenyl)aniline (AZNME). The n(NN) , ,*(NN) and ,(NN) , ,*(NN) transitions of the dyad were bathochromically shifted with a significant charge transfer character. The shifting of ,(NN) , ,*(NN) excitation energy closer to the n , ,*(NN) in comparison with the model aminoazobenzene emphasized the predominant existence of charge separated quinonoid-like ground state electronic structure. Increasing solvent polarity introduced hyperchromic effect in the ,(NN) , ,*(NN) electronic transition at the expense of transitions involved with benzenic states, and the extent of intensity borrowing was quantified adopting the Gaussian deconvolution method. On a comparative scale, the predicted excitation energies were in reasonable agreement with the observed values, demonstrating the efficiency of TD-DFT in predicting the localized and the charge transfer nature of transitions involved with large electronically asymmetric molecules with HOMO and LUMO centered on different parts of the molecular framework. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010 [source]

    Efficient calculation of configurational entropy from molecular simulations by combining the mutual-information expansion and nearest-neighbor methods,,

    Vladimir Hnizdo
    Abstract Changes in the configurational entropies of molecules make important contributions to the free energies of reaction for processes such as protein-folding, noncovalent association, and conformational change. However, obtaining entropy from molecular simulations represents a long-standing computational challenge. Here, two recently introduced approaches, the nearest-neighbor (NN) method and the mutual-information expansion (MIE), are combined to furnish an efficient and accurate method of extracting the configurational entropy from a molecular simulation to a given order of correlations among the internal degrees of freedom. The resulting method takes advantage of the strengths of each approach. The NN method is entirely nonparametric (i.e., it makes no assumptions about the underlying probability distribution), its estimates are asymptotically unbiased and consistent, and it makes optimum use of a limited number of available data samples. The MIE, a systematic expansion of entropy in mutual information terms of increasing order, provides a well-characterized approximation for lowering the dimensionality of the numerical problem of calculating the entropy of a high-dimensional system. The combination of these two methods enables obtaining well-converged estimations of the configurational entropy that capture many-body correlations of higher order than is possible with the simple histogramming that was used in the MIE method originally. The combined method is tested here on two simple systems: an idealized system represented by an analytical distribution of six circular variables, where the full joint entropy and all the MIE terms are exactly known, and the R,S stereoisomer of tartaric acid, a molecule with seven internal-rotation degrees of freedom for which the full entropy of internal rotation has been already estimated by the NN method. For these two systems, all the expansion terms of the full MIE of the entropy are estimated by the NN method and, for comparison, the MIE approximations up to third order are also estimated by simple histogramming. The results indicate that the truncation of the MIE at the two-body level can be an accurate, computationally nondemanding approximation to the configurational entropy of anharmonic internal degrees of freedom. If needed, higher-order correlations can be estimated reliably by the NN method without excessive demands on the molecular-simulation sample size and computing time. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008 [source]

    Competitive effects of grasses and woody plants in mixed-grass prairie

    JOURNAL OF ECOLOGY, Issue 4 2001
    Duane A. Peltzer
    Summary 1,Variation in the competitive ability of plant species may determine their persistence and abundance in communities. We quantified the competitive effects of grasses and woody plants in native mixed-grass prairie on the performance of transplant species and on resources. 2,We separated the effects of grasses, shrubs and intact vegetation containing both grasses and shrubs by manipulating the natural vegetation using selective herbicides to create four neighbourhood treatments: no neighbours (NN), no shrubs (NS), no grasses (NG) and all neighbours (AN). Treatments were applied to 2 × 2 m experimental plots located in either grass- or shrub-dominated habitats. The effects of grasses and shrubs on resource availability (light, soil moisture, soil available nitrogen) and on the growth of transplants of Bouteloua gracilis, a perennial tussock grass, and Elaeagnus commutata, a common shrub, were measured over two growing seasons. 3,Resource availability was two- to fivefold higher in no neighbour (NN) plots than in vegetated plots (NS, NG, AN) with grasses and shrubs having similar effects. Light penetration declined linearly with increasing grass or shrub biomass, to a minimum of about 30% incident light at 500 g m,2 shoot mass. Soil resources did not decline with increasing neighbour shoot or root mass for either grasses or shrubs, suggesting that the presence of neighbours was more important than their abundance. 4,Transplant growth was significantly suppressed by the presence of neighbours, but not by increasing neighbour shoot or root biomass, except for a linear decline in Bouteloua growth with increasing neighbour shoot mass in plots containing only shrubs. Competition intensity, calculated as the reduction in transplant growth by neighbours, was similar in both grass- and shrub-dominated habitats for transplants of Bouteloua, but was less intense in shrub-dominated habitats for the shrub Elaeagnus. Variation in the persistence and abundance of plants in communities may therefore be more strongly controlled by variation in the competitive effects exerted by neighbours than by differences in competitive response ability. [source]

    Dynamic process programming for a robotic manipulator based on hopfield NN monotonous optimization searching

    Zhongwei Yu
    A new approach to programming the optimal dynamic process for an n -joint rigid robotic manipulator with the use of the monotonous optimization searching ability of a Hopfield NN is presented. By combining robotic dynamics, this paper designs a programmed controller, which satisfies the aforementioned dynamic process. The convergence of the programmed controller is investigated. Simulations and experiments demonstrate the effectiveness of the scheme described. © 2004 Wiley Periodicals, Inc. [source]

    Forecasting interest rate swap spreads using domestic and international risk factors: evidence from linear and non-linear models

    Ilias Lekkos
    Abstract This paper explores the ability of factor models to predict the dynamics of US and UK interest rate swap spreads within a linear and a non-linear framework. We reject linearity for the US and UK swap spreads in favour of a regime-switching smooth transition vector autoregressive (STVAR) model, where the switching between regimes is controlled by the slope of the US term structure of interest rates. We compare the ability of the STVAR model to predict swap spreads with that of a non-linear nearest-neighbours model as well as that of linear AR and VAR models. We find some evidence that the non-linear models predict better than the linear ones. At short horizons, the nearest-neighbours (NN) model predicts better than the STVAR model US swap spreads in periods of increasing risk conditions and UK swap spreads in periods of decreasing risk conditions. At long horizons, the STVAR model increases its forecasting ability over the linear models, whereas the NN model does not outperform the rest of the models.,,Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Conformation-activity relationships of cyclo -constrained µ/, opioid agonists derived from the N -terminal tetrapeptide segment of dermorphin/deltorphin

    Sylwia Rodziewicz-Motowid
    Abstract The N -terminal tetrapeptide segments of dermorphin (Tyr,D -Ala,Phe,Gly,Tyr,Pro,Ser,NH2) and deltorphin (Tyr,D -Ala,Phe,Asp/Glu,Val,Val,Gly,NH2) are agonists at the opioid receptors µ and ,, respectively. [D -Arg2, Lys4]-dermorphin-(1,4) amide (Tyr,D -Arg,Phe,Lys,NH2, DALDA) and [Dmt1]DALDA (where Dmt is 2,,6,-dimethyltyrosine) are among the most potent and selective µ-agonists reported to date, both in vitro (having picomolar µ receptor affinity) and in vivo. In this communication, conformation-activity studies of the following four cyclic analogs of DALDA are presented and discussed: the lead peptide S2,S4 -cyclo (Tyr,D -Cys,Phe,Cys,NH2), constrained by means of an S4.2S4.4 disulfide between Cys2 and Cys4; its two cis and transC4.2C4.4 -olefinic dicarba analogs, and the product of saturation of them both. They are potent nonselective or moderately µ-selective opioid agonists in vitro. They have been synthesized and tested earlier [Berezowska I, Chung NN, Lemieux C, Wilkes BC, and Schiller PW, Acta Biochim Polon 53, 2006, 73,76]. We have studied their conformations using NMR and molecular dynamics. With major conformational constraints imposed by the 11-membered ring spanning residues 2,4, they show well defined conformations of this ring, while the exocylic Tyr1 and Phe3 side chains still have significant conformational freedom. The more active and selective µ versus , disulfide and saturated dicarba agonists seem to have in common: (i) their ring structures more flexilble than those of the other two and (ii) their ring structures similar to each other and more diverse than those in the other two. Given this and the small size of the peptides having confirmed bioactivity profiles, there is a chance that their conformations determined in solution approach receptor-bound conformations. Copyright © 2008 European Peptide Society and John Wiley & Sons, Ltd. [source]

    Robust circadian rhythm in heart rate and its variability: influence of exogenous melatonin and photoperiod

    Summary Heart rate (HR) and heart rate variability (HRV) undergo marked fluctuations over the 24-h day. Although controversial, this 24-h rhythm is thought to be driven by the sleep,wake/rest,activity cycle as well as by endogenous circadian rhythmicity. We quantified the endogenous circadian rhythm of HR and HRV and investigated whether this rhythm can be shifted by repeated melatonin administration while exposed to an altered photoperiod. Eight healthy males (age 24.4 ± 4.4 years) participated in a double-blind cross-over design study. In both conditions, volunteers were scheduled to 16 h,8 h rest : wake and dark : light cycles for nine consecutive days preceded and followed by 29-h constant routines (CR) for assessment of endogenous circadian rhythmicity. Melatonin (1.5 mg) or placebo was administered at the beginning of the extended sleep opportunities. For all polysomnographically verified wakefulness periods of the CR, we calculated the high- (HF) and low- (LF) frequency bands of the power spectrum of the R,R interval, the standard deviation of the normal-to-normal (NN) intervals (SDNN) and the square root of the mean-squared difference of successive NN intervals (rMSSD). HR and HRV variables revealed robust endogenous circadian rhythms with fitted maxima, respectively, in the afternoon (16:36 hours) and in the early morning (between 05:00 and 06:59 hours). Melatonin treatment phase-advanced HR, HF, SDNN and rMSSD, and these shifts were significantly greater than after placebo treatment. We conclude that endogenous circadian rhythmicity influences autonomic control of HR and that the timing of these endogenous rhythms can be altered by extended sleep/rest episodes and associated changes in photoperiod as well as by melatonin treatment. [source]


    Carl E. Zipper
    ABSTRACT: Nine surface water-quality variables were analyzed for trend at 180 Virginia locations over the 1978 to 1995 period. Median values and seasonal Kendall's tau, a trend indicator statistic, were generated for dissolved oxygen saturation (DO), biochemical oxygen demand (BOD), pH (PH), total residue (TR), nonfilterable residue (NFR), nitrate-nitrite nitrogen (NN), total Kjeldahl nitrogen (TKN), total phosphorus (TP), and fecal coliform (FC) at each location. Each location was assigned to one of four physiographic regions, and mean state and regional medians and taus were calculated. Widespread BOD and NFR improvements were detected and FC improvements occurred in the state's western regions. TR and TKN exhibited predominantly increasing trends at locations throughout the state. BOD, TKN, NFR, and TR medians were higher at coastal locations than in other regions. NN, TKN, and TR exhibited predominantly increasing trends in regions with high median concentrations, while declining trends predominated in regions with relatively high BOD, FC, and NFR medians. Appalachian locations exhibited the greatest regional water-quality improvements for BOD, FC, NFR, and TKN. Factors responsible for regional differences appear to include geology, land use, and landscape features; these factors vary regionally. [source]

    A neural network-based approach to determine FDTD eigenfunctions in quantum devices

    Antonio Soriano
    Abstract This article combines a Neural Network (NN) algorithm with the Finite Difference Time Domain (FDTD) technique to estimate the eigenfunctions in quantum devices. A NN based on the Least Mean Squares (LMS) algorithm is combined with the FDTD technique to provide a first approach to the confined states in quantum wires. The proposed technique is in good agreement with analytical results and is more efficient than FDTD combined with the Fourier Transform. This technique is used to calculate a numerical approximation to the eigenfunctions associated to quantum wire potentials. The performance and convergence of the proposed technique are also presented in this article. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 2017,2022, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24562 [source]