1H NMR spectra of alcohols and diols in chloroform: DFT/GIAO calculation of chemical shifts

Proton nuclear magnetic resonance (NMR) shifts of aliphatic alcohols in chloroform have been computed on the basis of density functional theory, the solvent being included by the integral‐equation‐formalism polarisable continuum model of Gaussian 09. Relative energies of all conformers are calculated at the Perdew, Burke and Ernzerhof (PBE)0/6‐311+G(d,p) level, and NMR shifts by the gauge‐including atomic orbital method with the PBE0/6‐311+G(d,p) geometry and the cc‐pVTZ basis set. The 208 computed CH proton NMR shifts for 34 alcohols correlate very well with the experimental values, with a gradient of 1.00 ± 0.01 and intercept close to zero; the overall root mean square difference (RMSD) is 0.08 ppm. Shifts for CH protons of diols in chloroform are well correlated with the theoretical values for (isotropic) benzene, with similar gradient and intercept (1.02 ± 0.01, ?0.13 ppm), but the overall RMSD is slightly higher, 0.12 ppm. This approach generally gives slightly better results than the CHARGE model of Abraham et al. The shifts of unsaturated alcohols in benzene have been re‐examined with Gaussian 09, but the overall fit for CH protons is not improved, and OH proton shifts are worse. Shifts of vinyl protons in alkenols are systematically overestimated, and the correlation of computed shifts against the experimental data for unsaturated alcohols follows a quadratic equation. Splitting the 20 compounds studied into two sets, and applying empirical scaling based on the quadratic for the first set to the second set, gives an RMSD of 0.10 ppm. A multi‐standard approach gives a similar result. Copyright © 2014 John Wiley & Sons, Ltd.     

Field analysis based on the smoothly deformed Bragg fiber

A new kind of smoothly deformed Bragg fiber structure has been proposed in this paper, and the mode field characteristics about this fiber have been analyzed. By comparing the field at different coordinates along the propagation direction, the evolution of the field for the elliptical transverse faces and the circular one is obtained. The method has also been used to detect the behavior of the elliptical Gaussian beam. This research method can be used to analyze the thermal deformation of fiber.     

Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection

We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes’ inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in the online Appendix (see the “Supplementary Materials” section). We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white-matter microstructure at every voxel of the corpus callosum for hundreds of subjects.     

Influences of preconditioning on the mutual coherence and the restricted isometry property of Gaussian/Bernoulli measurement matrices

This article discusses the influence of preconditioning on the mutual coherence and the restricted isometry property of Gaussian or Bernoulli measurement matrices. The mutual coherence can be reduced by preconditioning, although it is fairly small due to the probability estimate of the event that it is less than any given number in (0, 1). This can be extended to a set that contains either of the two types of matrices with a high probability but a subset with Lebesgue measure zero. The numerical results illustrate the reduction in the mutual coherence of Gaussian or Bernoulli measurement matrices. However, the first property can be true after preconditioning for a large type of measurement matrices having the property of s-order restricted isometry and being full row rank. This leads to a better estimate of the condition number of the corresponding submatrices and a more accurate error estimate of the conjugate gradient methods for the least squares problems typically used in greedy-like recovery algorithms.     

Calibration of a simple cone-penetration model for snow

Numerical studies using the Material Point Method (MPM) have been conducted recently to model snow penetration tests for fine-grained and coarse-grained snows using small cones with diameters ranging from 2.5 mm to 4 mm, and cone half-angles between 15° and 45°. Although numerical studies have gained physical insight of these tests, due to the lengthy computation time needed for the MPM simulations, it is not feasible to use these simulations to develop a stochastic model to assess the large variations of the mechanical properties of snow typically shown in tests. In this paper, we present a simple and efficient physics-based analytical model based on equilibrium and a cavity expansion solution upon which a stochastic model is built to obtain calibrated material parameters for a Drucker–Prager (DP) model such that prediction of the model can be made. Sensitivity analysis of the analytical model indicates that cohesion and interfacial shear (friction) factor contribute significantly to the penetration hardness whereas the friction angle has little contribution. The calibrated material parameters are similar to those estimated via the MPM simulations. The quality of the stochastic model, when compared with test data, was assessed using four interval-based validation metrics with good results.     

Generalized Fourier–Feynman transforms and generalized convolution products on Wiener space

In this paper we define a more general convolution product of functionals on Wiener space and develop the fundamental relationships between the generalized Fourier–Feynman transform and the convolution product.     

Parametric signal fitting by gaussian peak adjustment: A new multivariate curve resolution method for non-bilinear voltammetric measurements

A new methodology based on the fitting of signals to parametric functions is proposed for the multivariate curve resolution (MCR) analysis of overlapping and peak-shaped voltammetric signals which progressively get broader or narrower and move along the potential axis, thus causing a dramatic loss of linearity. The method is based on the least squares fitting of gaussian functions at both sides of the peaks by using adjustable parameters for the peak height, position and symmetry. It consists of several home-made programs written in Matlab environment, which are freely available as supplementary material of the present work. The application to the systems Zn(II)–oxalate, and to the phytochelatin PC₅ in a wide pH range provides excellent results as compared to these of more conventional linear methods, which raises good expectations about future application to electrochemical and even non-electrochemical data.     

Adaptive smearing for Brillouin zone integration

We suggest a simple scheme for automatically determining the width parameter of smearing methods of the Brillouin zone integration in electronic structure calculation. The scheme retains one free parameter that at any time can be eliminated by choosing a denser k‐space mesh until the desired accuracy is obtained. The tests are carried out in the context of Methfessel‐Paxton smearing. This adaptive Gaussian smearing (AGS) is easily implemented, variational with respect to partial occupancies and free from spurious occupancies that are negative or larger than one. Its convergence properties are similar to those obtained with the modified tetrahedron method for energy resolution of ≥0.1 meV. © 2010 Wiley Periodicals, Inc. Int J Quantum Chem, 2011     

Rouse–Bueche theory and the calculation of the monomeric friction coefficient in a filled system

Direct experimental access to the monomeric friction coefficient (ζ₀) relies on the availability of a suitable polymer dynamics model. Thus far, no method has been suggested that is applicable to filled systems, such as filled rubbers or microphase‐segregated A–B–A thermoplastic elastomers (TPEs) at T_g,B < T < T_g,A. Building upon the procedure proposed by Ferry for entangled and unfilled polymer melts, the Rouse–Bueche theory is applied to an undiluted triblock copolymer to extract ζ₀ from the linear behavior in the rubber‐glass transition region, and to estimate the size of Gaussian submolecules. When compared at constant T – T_g, the matrix monomeric friction factor is consistent with the corresponding value for the homopolymer melt. In addition, the characteristic Rouse dimensions are in good agreement with independent estimates based on the Kratky–Porod worm‐like chain model. These results seem to validate the proposed approach for estimating ζ₀ in filled systems. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016 , 54, 1437–1442     

Bayesian dynamic financial networks with time-varying predictors

We propose a targeted and robust modeling of dependence in multivariate time series via dynamic networks, with time-varying predictors included to improve interpretation and prediction. The model is applied to financial markets, estimating effects of verbal and material cooperations.