General expression for probabilistic estimation of multiphase structure invariants in the case of a native protein and multiple derivatives. Application to estimates of the three-phase structure invariants

Concise probabilistic formulae with definite crystallographic implications are obtained from the distribution for eight three-phase structure invariants (3PSIs) in the case of a native protein and a heavy-atom derivative [Hauptman (1982). Acta Cryst. A38, 289-294] and from the distribution for 27 3PSIs in the case of a native and two derivatives [Fortier, Weeks & Hauptman (1984). Acta Cryst. A40, 646-651]. The main results of the probabilistic formulae for the four-phase structure invariants are presented and compared with those for the 3PSIs. The analysis directly leads to a general formula of probabilistic estimation for the n-phase structure invariants in the case of a native and m derivatives. The factors affecting the estimated accuracy of the 3PSIs are examined using the diffraction data from a moderate-sized protein. A method to estimate a set of the large-modulus invariants, each corresponding to one of the eight 3PSIs, that has the largest \Delta\ values and relatively large structure-factor moduli between the native and derivative is suggested, which remarkably improves the accuracy, and thus a phasing procedure making full use of all eight 3PSIs is proposed.

Inverse Class E amplifier and oscillator phase noise characteristics

This paper gives the first experimental characterisation of the phase noise response of the recently introduced Inverse Class E topology when operated as an amplifier and then as an oscillator. The results indicate that in amplifier and oscillator modes of operation conversion efficiencies of 64%, and 42% respectively are available, and that the excess PM noise added as a consequence of saturated Class E operation results in about a 10 dB increase in PM over that expected from a small-signal Class A amplifier operating at much lower efficiency. Inverse Class E phase transfer dependence on device drain bias and flicker noise are presented in order to show, respectively, that the Inverse Class E amplifier and oscillator follow the trends predicted by conventional phase noise theory. © 2007 EuMA.

Quasi-pilot aided phase noise estimation for coherent optical OFDM systems

In this letter, a novel phase noise estimation scheme has been proposed for coherent optical orthogonal frequency division multiplexing systems, the quasi-pilot-aided method. In this method, the phases of transmitted pilot subcarriers are deliberately correlated to the phases of data subcarriers. Accounting for this correlation in the receiver allows the required number of pilots needed for a sufficient estimation and compensation of phase noise to be reduced by a factor of 2 in comparison with the traditional pilot-aided phase noise estimation method. We carried out numerical simulation of a 40 Gb/s single polarization transmission system, and the outcome of the investigation indicates that by applying quasi-pilot-aided phase estimation, only four pilot subcarriers are needed for effective phase noise compensation. © 2014 IEEE.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.

Econometric Modelling with Time Series : Specification, Estimation and Testing

This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.

Correlation based method for phase identification in a three phase LV distribution network

Low voltage distribution networks feature a high degree of load unbalance and the addition of rooftop photovoltaic is driving further unbalances in the network. Single phase consumers are distributed across the phases but even if the consumer distribution was well balanced when the network was constructed changes will occur over time. Distribution transformer losses are increased by unbalanced loadings. The estimation of transformer losses is a necessary part of the routine upgrading and replacement of transformers and the identification of the phase connections of households allows a precise estimation of the phase loadings and total transformer loss. This paper presents a new technique and preliminary test results for a method of automatically identifying the phase of each customer by correlating voltage information from the utility's transformer system with voltage information from customer smart meters. The techniques are novel as they are purely based upon a time series of electrical voltage measurements taken at the household and at the distribution transformer. Experimental results using a combination of electrical power and current of the real smart meter datasets demonstrate the performance of our techniques.

复杂介质中地震波场数值模拟研究——基于广义正交多项式迭积微分算子法

In this paper, we propose a new numerical modeling method – Convolutional Forsyte Polynomial Differentiator (CFPD), aimed at simulating seismic wave propagation in complex media with high efficiency and accuracy individually owned by short-scheme finite differentiator and general convolutional polynomial method. By adjusting the operator length and optimizing the operator coefficient, both global and local informations can be easily incorporated into the wavefield which is important to invert the undersurface geological structure. The key issue in this paper is to introduce the convolutional differentiator based on Forsyte generalized orthogonal polynomial in mathematics into the spatial differentiation of the first velocity-stress equation. To match the high accuracy of the spatial differentiator, this method in the time coordinate adopts staggered grid finite difference instead of conventional finite difference to model seismic wave propagation in heterogeneous media. To attenuate the reflection artifacts caused by artificial boundary, Perfectly Matched Layer (PML) absorbing boundary is also being considered in the method to deal with boundary problem due to its advantage of automatically handling large-angle emission. The PML formula for acoustic equation and first-order velocity-stress equation are also derived in this paper. There is little difference to implement the PML boundary condition in all kind of wave equations, but in Biot media, special attenuation factors should be taken. Numerical results demonstrate that the PML boundary condition is better than Cerjan absorbing boundary condition which makes it more suitable to hand the artificial boundary reflection. Based on the theories of anisotropy, Biot two-phase media and viscous-elasticity, this paper constructs the constitutive relationship for viscous-elastic and two-phase media, and further derives the first-order velocity-stress equation for 3D viscous-elastic and two-phase media. Numerical modeling using CFPD method is carried out in the above-mentioned media. The results modeled in the viscous-elastic media and the anisotropic pore elastic media can better explain wave phenomena of the true earth media, and can also prove that CFPD is a useful numerical tool to study the wave propagation in complex media.

A DFT Guided/Experimental Approach to Asymmetric Allylation and Phase-Transfer Catalysis

The Dudding group is interested in the application of Density Functional Theory (DFT) in developing asymmetric methodologies, and thus the focus of this dissertation will be on the integration of these approaches. Several interrelated subsets of computer aided design and implementation in catalysis have been addressed during the course of these studies. The first of the aims rested upon the advancement of methodologies for the synthesis of biological active C(1)-chiral 3-methylene-indan-1-ols, which in practice lead to the use of a sequential asymmetric Yamamoto-Sakurai-Hosomi allylation/Mizoroki Heck reaction sequence. An important aspect of this work was the utilization of ortho-substituted arylaldehyde reagents which are known to be a problematic class of substrates for existing asymmetric allylation approaches. The second phase of my research program lead to the further development of asymmetric allylation methods using o-arylaldehyde substrates for synthesis of chiral C(3)-substituted phthalides. Apart from the de novo design of these chemistries in silico, which notably utilized water-tolerant, inexpensive, and relatively environmental benign indium metal, this work represented the first computational study of a stereoselective indium-mediated process. Following from these discoveries was the advent of a related, yet catalytic, Ag(I)-catalyzed approach for preparing C(3)-substituted phthalides that from a practical standpoint was complementary in many ways. Not only did this new methodology build upon my earlier work with the integrated (experimental/computational) use of the Ag(I)-catalyzed asymmetric methods in synthesis, it provided fundamental insight arrived at through DFT calculations, regarding the Yamamoto-Sakurai-Hosomi allylation. The development of ligands for unprecedented asymmetric Lewis base catalysis, especially asymmetric allylations using silver and indium metals, followed as a natural extension from these earlier discoveries. To this end, forthcoming as well was the advancement of a family of disubstituted (N-cyclopropenium guanidine/N-imidazoliumyl substituted cyclopropenylimine) nitrogen adducts that has provided fundamental insight into chemical bonding and offered an unprecedented class of phase transfer catalysts (PTC) having far-reaching potential. Salient features of these disubstituted nitrogen species is unprecedented finding of a cyclopropenium based C-H•••πaryl interaction, as well, the presence of a highly dissociated anion projected them to serve as a catalyst promoting fluorination reactions. Attracted by the timely development of these disubstituted nitrogen adducts my last studies as a PhD scholar has addressed the utility of one of the synthesized disubstituted nitrogen adducts as a valuable catalyst for benzylation of the Schiff base N-diphenyl methylene glycine ethyl ester. Additionally, the catalyst was applied for benzylic fluorination, emerging from this exploration was successful fluorination of benzyl bromide and its derivatives in high yields. A notable feature of this protocol is column-free purification of the product and recovery of the catalyst to use in a further reaction sequence.

Covariance matrix formula for Birnbaum-Saunders regression models

The Birnbaum-Saunders regression model is commonly used in reliability studies. We derive a simple matrix formula for second-order covariances of maximum-likelihood estimators in this class of models. The formula is quite suitable for computer implementation, since it involves only simple operations on matrices and vectors. Some simulation results show that the second-order covariances can be quite pronounced in small to moderate sample sizes. We also present empirical applications.

Speed estimation for induction motor using neural networks method

This work presents an alternative approach based on neural network method in order to estimate speed of induction motors, using the measurement of primary variables such as voltage and current. Induction motors are very common in many sectors of the industry and assume an important role in the national energy policy. The nowadays methodologies, which are used in diagnosis, condition monitoring and dimensioning of these motors, are based on measure of the speed variable. However, the direct measure of this variable compromises the system control and starting circuit of an electric machinery, reducing its robustness and increasing the implementation costs. Simulation results and experimental data are presented to validate the proposed approach. © 2003-2012 IEEE.

A rescaling of the phase space for Hamiltonian map: Applications on the Kepler map and mappings with diverging angles in the limit of vanishing action

A rescale of the phase space for a family of two-dimensional, nonlinear Hamiltonian mappings was made by using the location of the first invariant Kolmogorov-Arnold-Moser (KAM) curve. Average properties of the phase space are shown to be scaling invariant and with different scaling times. Specific values of the control parameters are used to recover the Kepler map and the mapping that describes a particle in a wave packet for the relativistic motion. The phase space observed shows a large chaotic sea surrounding periodic islands and limited by a set of invariant KAM curves whose position of the first of them depends on the control parameters. The transition from local to global chaos is used to estimate the position of the first invariant KAM curve, leading us to confirm that the chaotic sea is scaling invariant. The different scaling times are shown to be dependent on the initial conditions. The universality classes for the Kepler map and mappings with diverging angles in the limit of vanishing action are defined. © 2013 Published by Elsevier Inc. All rights reserved.

Generalized Scaling Factor for Estimating the Activity Concentrations of Difficult-to-Measure Nuclides

2002 Mathematics Subject Classification: 62P35, 62P30.

World emissions and economic growth: Application of non-parametric methods

The Environmental Kuznets Curve (EKC) hypothesises an inverse U-shaped relationship between a measure of environmental pollution and per capita income levels. In this study, we apply non-parametric estimation of local polynomial regression (local quadratic fitting) to allow more flexibility in local estimation. This study uses a larger and globally representative sample of many local and global pollutants and natural resources including Biological Oxygen Demand (BOD) emission, CO2 emission, CO2 damage, energy use, energy depletion, mineral depletion, improved water source, PM10, particulate emission damage, forest area and net forest depletion. Copyright © 2009 Inderscience Enterprises Ltd.

Thermal degradation of polystyrene

The importance of the study of thermal degradation of polymeric fuels arises from their role in the combustion of solid propellants. Estimation of the condensed-phase heat release during combustion can be facilitated by the knowledge of the enthalpy change associated with the polymer degradation process. Differential scanning calorimetry has been used to obtain enthalpy data. Kinetic studies on the polymeric degradation process have been carried out with the following objectives. The literature values of activation energies are quite diverse and differ from author to author. The present study has tried to locate possible reasons for the divergence in the reported activation energy values. A value of 30 kcal has been obtained and found to be independent of the technique employed. The present data on the kinetics support to chain-end initiation and unzipping process. The activation energies are further found to be independent of the atmosphere in which the degradation of polymer fuel is carried out. The degradation in air, N2, and O2 all yield a value of 30 kcal/mole for the activation energies.

Reduced Grobner bases and Macaulay-Buchberger Basis Theorem over Noetherian rings

In this paper, we extend the characterization of Zx]/(f), where f is an element of Zx] to be a free Z-module to multivariate polynomial rings over any commutative Noetherian ring, A. The characterization allows us to extend the Grobner basis method of computing a k-vector space basis of residue class polynomial rings over a field k (Macaulay-Buchberger Basis Theorem) to rings, i.e. Ax(1), ... , x(n)]/a, where a subset of Ax(1), ... , x(n)] is an ideal. We give some insights into the characterization for two special cases, when A = Z and A = ktheta(1), ... , theta(m)]. As an application of this characterization, we show that the concept of Border bases can be extended to rings when the corresponding residue class ring is a finitely generated, free A-module. (C) 2014 Elsevier B.V. All rights reserved.