Validation of a semi-Lagrangian, canonical regression model of climates in the southwestern United States

EXTRACT (SEE PDF FOR FULL ABSTRACT): A local climate model (LCM) has been developed to simulate the modern and 18 ka climate of the southwestern United States. ... LCM solutions indicate summers were about 1°C cooler and winters 11°C cooler at 18 ka. Annual PREC increased 68% at 18 ka, with large increases in spring and fall PREC and diminished summer monsoonal PREC. ... Validation of simulations of 18 ka climate indicate general agreement with proxy estimates of climate for that time. However, the LCM estimates of summer temperatures are about 5 to 10°C higher than estimates from proxy reconstructions.

A comparison of losses in small (<1 kW) drives using sine and space vector pulse width modulation schemes

this paper quantifies effects of using three different pulse width modulation (PWM) schemes on the losses in the inverter and induction motor of a 1 kW drive. Direct measurements of losses have been made with a calorimeter. Results show that for the inverter, discontinuous PWM excitation reduces losses by up to 15% compared to sine and symmetrical space vector PWM methods. However, at a low modulation index the greater harmonic content with discontinuous PWM increased motor losses by nearly 20%. This study demonstrates the importance of careful choice of modulation scheme to achieve high overall drive efficiency. © 2005 IEEE.

Gaussian Process Regression Networks

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.

Application of the fixed point method for solution in time stepping finite element analysis using the inverse vector Jiles-Atherton model

An implementation of the inverse vector Jiles-Atherton model for the solution of non-linear hysteretic finite element problems is presented. The implementation applies the fixed point method with differential reluctivity values obtained from the Jiles-Atherton model. Differential reluctivities are usually computed using numerical differentiation, which is ill-posed and amplifies small perturbations causing large sudden increases or decreases of differential reluctivity values, which may cause numerical problems. A rule based algorithm for conditioning differential reluctivity values is presented. Unwanted perturbations on the computed differential reluctivity values are eliminated or reduced with the aim to guarantee convergence. Details of the algorithm are presented together with an evaluation of the algorithm by a numerical example. The algorithm is shown to guarantee convergence, although the rate of convergence depends on the choice of algorithm parameters. © 2011 IEEE.

In vitro construction of a recombinant human embryonic brain-derived neurotrophin-4 gene and pEGFP-N1 vector

BACKGROUND: Neurotrophin-4 (NT-4) can promote neuronal growth, development, differentiation, maturation, and survival. NT-4 can also improve recovery and regeneration of injured neurons, but cannot pass through the blood-brain barrier, which limits its ac