Interface pattern formation in nonlinear dissipative systems

The problem of interface pattern selection in nonlinear dissipative systems is critical in many fields of science, occurring in physical, chemical and biological systems. One of the simplest pattern formations is the Saffman-Taylor finger pattern that forms when a viscous fluid is displaced by a less viscous fluid. Such finger-shaped patterns have been observed in distinctly different fields of science (hydrodynamics, combustion and crystal growth) and this has led to a search for a unified concept of pattern formation, as first proposed by the classic work of D'arcy Thomson. Two-dimensional finger-shaped patterns, observed in flame fronts and the ensembled average shape of the diffusion-limited aggregation pattern, have been shown to be similar to Saffman-Taylor finger shapes. Here we present experimental studies that establish that the cell shapes formed during directional solidification of alloys can be described by the form of the Saffman-Taylor finger shape equation when a second phase is present in the intercellular region.     

Effect of thermosolutal convection on microstructure formation in the Pb-Bi peritectic system

Experimental studies have been conducted in the two-phase region of the Pb-Bi peritectic system to investigate the effect of thermosolutal convection on the banded microstructure. A systematic study is carried out by varying convection effects through the use of thin cylindrical samples of different diameters. A strong oscillatory convection is found in a 6.0-mm-diameter sample that produces a large treelike primary phase in the center of the sample that is surrounded by the peritectic phase matrix. The length of the treelike structure is found to decrease as the diameter of a sample is reduced to 0.8 mm. When the sample diameter is further reduced to 0.4 mm, laminar flow is present that gives rise to discrete bands of the two phases. The banded microstructure, however, is found to be transient and only the peritectic phase forms after a few bands. Composition variations in the banded structure are measured to determine the nucleation undercoolings for both phases and to characterize the banding cycle. The banding cycle is determined by the nucleation undercoolings and is independent of convection in the melt, but the banding window closely depends on convection. The presence of the transient banding process is analyzed by using a boundary layer model, and the number of transient bands is found to agree with the model for samples of different compositions and lengths.     

Time-resolved Fourier transform infrared spectroscopic imaging

Fourier transform infrared (FT-IR) imaging allows simultaneous spectral characterization of large spatial areas due to its multichannel detection advantage. The acquisition of large amounts of data in the multichannel configuration results, however, in a poor temporal resolution of sequentially acquired data sets, which limits the examination of dynamic processes to processes that have characteristic time scales of the order of minutes. Here, we introduce the concept and instrumental details of a time-resolved infrared spectroscopic imaging modality that permits the examination of repetitive dynamic processes whose half-lives are of the order of milli-seconds. As an illustration of this implementation of step-scan FT-IR imaging, we examine the molecular responses to external electric-field perturbations of a microscopically heterogeneous polymer-liquid crystal composite. Analysis of the spectroscopic data using conventional univariate and generalized two-dimensional (2D) correlation methods emphasizes an additional capability for accessing of simultaneous spatial and temporal chemical measurements of molecular dynamic processes.     

A continuous-flow system for high-precision kinetics using small volumes

A generally applicable continuous-flow kinetic analysis system that gives data of a precision high enough to measure small kinetic isotope effects for enzymatic and nonenzymatic reactions is described. It employs commercially available components that are readily assembled into an apparatus that is easy to use. It operates under laminar flow conditions, which requires that the time between the initiation of the reaction in the mixer and the observation be long enough that molecular diffusion can effect a symmetrization of the concentration profile that results from a thin plug of reagents introduced at the mixer. The analysis of a second-order irreversible reaction under pseudo-first-order conditions is presented. The Yersinia pestis protein tyrosine phosphatase catalyzed hydrolysis of p-nitrophenyl phosphate is characterized with the system, and a proton inventory on kcat is presented.     

Two-stage Robust Network Design with Exponential Scenarios

We study two-stage robust variants of combinatorial optimization problems on undirected graphs, like Steiner tree, Steiner forest, and uncapacitated facility location. Robust optimization problems, previously studied by Dhamdhere et al. (Proc. of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 367–378, 2005), Golovin et al. (Proc. of the 23rd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 2006), and Feige et al. (Proc. of the 12th International Integer Programming and Combinatorial Optimization Conference, pp. 439–453, 2007), are two-stage planning problems in which the requirements are revealed after some decisions are taken in Stage 1. One has to then complete the solution, at a higher cost, to meet the given requirements. In the robust k-Steiner tree problem, for example, one buys some edges in Stage 1. Then k terminals are revealed in Stage 2 and one has to buy more edges, at a higher cost, to complete the Stage 1 solution to build a Steiner tree on these terminals. The objective is to minimize the total cost under the worst-case scenario. In this paper, we focus on the case of exponentially many scenarios given implicitly. A scenario consists of any subset of k terminals (for k-Steiner tree), or any subset of k terminal-pairs (for k-Steiner forest), or any subset of k clients (for facility location). Feige et al. (Proc. of the 12th International Integer Programming and Combinatorial Optimization Conference, pp. 439–453, 2007) give an LP-based general framework for approximation algorithms for a class of two stage robust problems. Their framework cannot be used for network design problems like k-Steiner tree (see later elaboration). Their framework can be used for the robust facility location problem, but gives only a logarithmic approximation. We present the first constant-factor approximation algorithms for the robust k-Steiner tree (with exponential number of scenarios) and robust uncapacitated facility location problems. Our algorithms are combinatorial and are based on guessing the optimum cost and clustering to aggregate nearby vertices. For the robust k-Steiner forest problem on trees and with uniform multiplicative increase factor for Stage 2 (also known as inflation), we present a constant approximation. We show APX-hardness of the robust min-cut problem (even with singleton-set scenarios), resolving an open question of (Dhamdhere et al. in Proc. of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 367–378, 2005) and (Golovin et al. in Proc. of the 23rd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 2006).     

The mean squared error of Geweke and Porter-Hudak's estimator of the memory parameter of a long-memory time series

We establish some asymptotic properties of a log-periodogram regression estimator for the memory parameter of a long-memory time series. We consider the estimator originally proposed by Geweke and Porter-Hudak (The estimation and application of long memory time series models. Journal of Time Ser. Anal. 4 (1983), 221–37). In particular, we do not omit any of the low frequency periodogram ordinates from the regression. We derive expressions for the estimator's asymptotic bias, variance and mean squared error as functions of the number of periodogram ordinates, m , used in the regression. Consistency of the estimator is obtained as long as m ←∞ and n ←∞ with ( m log m )/ n ← 0, where n is the sample size. Under these and the additional conditions assumed in this paper, the optimal m , minimizing the mean squared error, is of order O( n ^4/5). We also establish the asymptotic normality of the estimator. In a simulation study, we assess the accuracy of our asymptotic theory on mean squared error for finite sample sizes. One finding is that the choice m = n ^1/2, originally suggested by Geweke and Porter-Hudak (1983), can lead to performance which is markedly inferior to that of the optimal choice, even in reasonably small samples.     

Metal oxide nanoparticles for advanced energy applications

Hot-wire chemical vapor deposition (HWCVD) has been employed as an economically scalable method for the deposition of crystalline molybdenum oxide nanoparticles at high density. Under optimal synthesis conditions, only crystalline nanostructures with a smallest dimension of ~ 3-50 nm are observed with extensive transmission electron microscopy analyses. The incorporation of crystalline molybdenum oxide nanoparticles into battery electrodes has led to profound advancements in state-of-the-art negative electrodes (anodes) in lithium-ion batteries. The nanoparticle materials exhibit a high rate capability as anticipated for the reduced solid-state Li-ion diffusion length.     

Plug-in Selection of the Number of Frequencies in Regression Estimates of the Memory Parameter of a Long-memory Time Series

We consider the problem of selecting the number of frequencies, m , in a log-periodogram regression estimator of the memory parameter d of a Gaussian long-memory time series. It is known that under certain conditions the optimal m , minimizing the mean squared error of the corresponding estimator of d , is given by m ^(opt)= Cn ^4/5, where n is the sample size and C is a constant. In practice, C would be unknown since it depends on the properties of the spectral density near zero frequency. In this paper, we propose an estimator of C based again on a log-periodogram regression and derive its consistency. We also derive an asymptotically valid confidence interval for d when the number of frequencies used in the regression is deterministic and proportional to n ^4/5. In this case, squared bias cannot be neglected since it is of the same order as the variance. In a Monte Carlo study, we examine the performance of the plug-in estimator of d , in which m is obtained by using the estimator of C in the formula for m ^(opt) above. We also study the performance of a bias-corrected version of the plug-in estimator of d . Comparisons with the choice m = n ^1/2 frequencies, as originally suggested by Geweke and Porter-Hudak (The estimation and application of long memory time series models. Journal of Time Ser. Anal. 4 (1983), 221–37), are provided.     

Effective parameter estimation within a multi-dimensional population balance model framework

This study considers optimization problems with multi-dimensional population balance models embedded. The objective function is formulated as a least-squares problem, minimizing the difference between target data and simulated model output and the goal is to find model parameter values that best fit the data. Results show that derivative-free methods, such as the Nelder–Mead simplex method, fail to converge to an optimal solution. A similar result was obtained with gradient-based methods such as BFGS, quasi-Newton, Newton, Gauss–Newton, Levenberg–Marquardt and SQP, and with a stochastic genetic algorithm. It was hypothesized that three main issues could contribute to these convergence failures: (1) gradients were calculated based on finite differences, and as a result of improper step size determination, the numerical error could be prohibitive resulting in inaccurate derivative information, (2) the parameters may not be identifiable and (3) numerical instability could occur during the course of optimization. To circumvent these issues, this work addresses the calculation of derivative information based on automatic differentiation and sensitivity analysis to ensure increased accuracy. Issues such as parameter identifiability are also dealt with by analyzing an accurate Fisher information matrix. Given the computational burden in calculating accurate Jacobians and Hessians, compounded by the potential nonsmoothness introduced into the objective function as a result of granule nucleation, other optimization strategies may be warranted and this work addresses those accordingly. Overall, by systematically assessing the problem formulation and mechanisms, the results show that substantial improvements in convergence can be achieved by utilizing appropriate optimization techniques, thus leading to more successful and optimal parameter estimation.