Lipophilicity study of eight cephalosporins by reversed‐phase thin‐layer chromatographic method

The lipophilicity (R_M0) and specific hydrophobic surface area for the representatives of four generation cephalosporins have been determined by reversed‐phase thin‐layer chromatography, and the effect of different mobile‐phase modifiers (such as methanol, acetonitrile, acetone, 1,4‐dioxane and 2‐propanol) on the retention has been studied. The compounds studied showed typical retention behavior; their R_M values decreased linearly with increasing concentration of the organic modifier in the eluent. The linear correlations between the volume fraction of the organic solvent and the R_M values over a limited range were established for each solute, resulting in high values of correlation coefficients (>0.95 in most cases). R_M values were determined by various concentrations of organic modifier, and the correlation obtained was extrapolated to 0% of organic modifier. Chromatographically established logP (R_M0) parameters were compared with computationally calculated partition coefficients values (AClogP, ALOGP, KOWWIN, ALOGPs, XLOGP2, MLOGP and XLOGP3) and experimental octanol–water logP values (measured by the shake flask method). The received results demonstrate that RP‐TLC may be a good alternative technique for analytics in describing the lipophilic nature of investigated cephalosporins as well as the activity. Copyright © 2015 John Wiley & Sons, Ltd.     

Supercapacitive Swing Adsorption of Carbon Dioxide

An electrical effect, the supercapacitive swing adsorption (SSA) effect is reported, which allows for reversible adsorption and desorption of carbon dioxide by capacitive charge and discharge of electrically conducting porous carbon materials. The SSA effect can be observed when an electrically conducting, nanoporous carbon material is brought into contact with carbon dioxide gas and an aqueous electrolyte. Charging the supercapacitor electrodes initiates the spontaneous organization of electrolyte ions into an electric double layer at the surface of each porous electrode. The presence of this double layer leads to reversible, selective uptake and release of the CO₂ as the supercapacitor is charged and discharged.     

Empirical wavelet analysis of tail and memory properties of LARCH and FIGARCH models

Using computationally efficient wavelet methods, we study two nonlinear models of financial returns {r _t }: linear ARCH (LARCH) and fractionally integrated GARCH (FIGARCH). We estimate the tail index α and the long memory parameter d of the squared returns X_t = r_t²{X_t= r_t^2} of LARCH, and of the powers X _t = |r _t | ^p of FIGARCH. We find that the X _t have infinite variance and long memory, and show how the estimates of α and d depend on the model parameters. These relationships are determined empirically, as the setting is quite complex, and no suitable theory has been developed so far. In particular, we provide empirical relationships between the estimates [^(d)]{\hat d} and the difference parameters in LARCH and FIGARCH. Our computational work uncovers tail and memory properties of LARCH and FIGARCH for practically relevant parameter ranges, and provides some guidance on modeling returns on speculative assets including FX rates, stocks and market indices.     

EFR frequency dependent linewidths in mixed valence complexes: The 103 effect

EPR measurements on hexamminecobalt (III) and hexamminechromium (III) chlorocupratest (I,II) have revealed linewidth variations with observation freque     

Robust Wavelet-Domain Estimation of the Fractional Difference Parameter in Heavy-Tailed Time Series: An Empirical Study

We investigate the performance of several wavelet-based estimators of the fractional difference parameter. We consider situations where, in addition to long-range dependence, the time series exhibit heavy tails and are perturbed by polynomial and change-point trends. We make detailed study of a wavelet-domain pseudo Maximum Likelihood Estimator (MLE), for which we provide an asymptotic and finite-sample justification. Using numerical experiments, we show that unlike the traditional time-domain estimators, estimators based on the wavelet transform are robust to additive trends and change points in mean, and produce accurate estimates even under significant departures from normality. The Wavelet-domain MLE appears to dominate a regression-based wavelet estimator in terms of smaller root mean squared error. These findings are derived from a simulation study and application to computer traffic traces.     

Asymptotic normality of the principal components of functional time series

We establish the asymptotic normality of the sample principal components of functional stochastic processes under nonrestrictive assumptions which admit nonlinear functional time series models. We show that the aforementioned asymptotic depends only on the asymptotic normality of the sample covariance operator, and that the latter condition holds for weakly dependent functional time series which admit expansions as Bernoulli shifts. The weak dependence is quantified by the condition of L⁴$L^4$-m$m$-approximability which includes all functional time series models in practical use. We also demonstrate convergence of the cross covariance operators of the sample functional principal components to their counterparts in the normal limit.     

Empirical properties of forecasts with the functional autoregressive model

We study the finite sample performance of predictors in the functional (Hilbertian) autoregressive model X_n+1 = Y(X_n)+e_n{X_{n+1} = \Psi(X_n)+\varepsilon_n}. Our extensive empirical study based on simulated and real data reveals that predictors of the form [^(Y)](X_n){\hat\Psi(X_n)} are practically optimal in a sense that their prediction errors are comparable with those of the infeasible perfect predictor Ψ(X _n ). The predictions [^(Y)](X_n){\hat\Psi(X_n)} cannot be improved by an improved estimation of Ψ, nor by a more refined prediction approach which uses predictive factors rather than the functional principal components. We also discuss the practical limits of predictions that are feasible using the functional autoregressive model. These findings have not been established by theoretical work currently available, and may serve as a practical reference to the properties of predictors of functional data.     

Testing for parameter changes in ARCH models

The paper develops the asymptotic theory for CUSUM-type tests for a change point in parameters of an ARCH(∞) model. Special attention is given to asymptotics under local alternatives. Research partially supported by EPSRC grant GR/L/78222 from the University of Liverpool. Partially supported by NATO grant CRG 960503. Partially supported by the Lithuanian State Science and Studies Foundation, Grant K-014. Published in Lietuvos Matematikos Rinkinys, Vol. 39, No. 2, pp. 231–247, April–June, 1999.     

New classes of self-similar symmetric stable random fields

We construct two new classes of symmetric stable self-similar random fields with stationary increments, one of the moving average type, the other of the harmonizable type. The fields are defined through an integral representation whose kernel involves a norm on ⁿ . We examine how the choice of the norm affects the finite-dimensional distributions. We also study the processes which are obtained by projecting the random fields on a one-dimensional subspace. We compare these projection processes with each other and with other well-known self-similar processes and we characterize their asymptotic dependence structure.The research was done at Boston University while the first author was on leave from the Hugo Steinhaus Center, Poland. The second author was partially supported by the ONR Grant N00014-90-J-1287 at Boston University and by a grant of the United States-Israel Binational Science Foundation.     

Approximation for bootstrapped empirical processes

We obtain an approximation for the bootstrapped empirical process with the rate of the Komlós, Major and Tusnády approximation for empirical processes. The proof of the new approximation is based on the Poisson approximation for the uniform empirical distribution function and the Gaussian approximation for randomly stopped sums.