Ordering dynamics of the driven lattice gas model

The evolution of a two-dimensional driven lattice-gas model is studied on an L_x X L_y lattice. Scaling arguments and extensive numerical simulations are used to show that starting from random initial configuration the model evolves via two stages: (a) an early stage in which alternating stripes of particles and vacancies are formed along the direction y of the driving field, and (b) a stripe coarsening stage, in which the number of stripes is reduced and their average width increases. The number of stripes formed at the end of the first stage is shown to be a function of L_x/L_y^\phi, with \phi ~ 0.2. Thus, depending on this parameter, the resulting state could be either single or multi striped. In the second, stripe coarsening stage, the coarsening time is found to be proportional to L_y, becoming infinitely long in the thermodynamic limit. This implies that the multi striped state is thermodynamically stable. The results put previous studies of the model in a more general framework

Phase Separation Kinetics in a Model with Order-Parameter Dependent Mobility

We present extensive results from 2-dimensional simulations of phase separation kinetics in a model with order-parameter dependent mobility. We find that the time-dependent structure factor exhibits dynamical scaling and the scaling function is numerically indistinguishable from that for the Cahn-Hilliard (CH) equation, even in the limit where surface diffusion is the mechanism for domain growth. This supports the view that the scaling form of the structure factor is "universal" and leads us to question the conventional wisdom that an accurate representation of the scaled structure factor for the CH equation can only be obtained from a theory which correctly models bulk diffusion.Comment: To appear in PRE, figures available on reques

Evolution of speckle during spinodal decomposition

Time-dependent properties of the speckled intensity patterns created by scattering coherent radiation from materials undergoing spinodal decomposition are investigated by numerical integration of the Cahn-Hilliard-Cook equation. For binary systems which obey a local conservation law, the characteristic domain size is known to grow in time $\tau$ as $R = [B \tau]^n$ with n=1/3, where B is a constant. The intensities of individual speckles are found to be nonstationary, persistent time series. The two-time intensity covariance at wave vector ${\bf k}$ can be collapsed onto a scaling function $Cov(\delta t,\bar{t})$, where $\delta t = k^{1/n} B |\tau_2-\tau_1|$ and $\bar{t} = k^{1/n} B (\tau_1+\tau_2)/2$. Both analytically and numerically, the covariance is found to depend on $\delta t$ only through $\delta t/\bar{t}$ in the small-$\bar{t}$ limit and $\delta t/\bar{t} ^{1-n}$ in the large-$\bar{t}$ limit, consistent with a simple theory of moving interfaces that applies to any universality class described by a scalar order parameter. The speckle-intensity covariance is numerically demonstrated to be equal to the square of the two-time structure factor of the scattering material, for which an analytic scaling function is obtained for large $\bar{t}.$ In addition, the two-time, two-point order-parameter correlation function is found to scale as $C(r/(B^n\sqrt{\tau_1^{2n}+\tau_2^{2n}}),\tau_1/\tau_2)$, even for quite large distances $r$. The asymptotic power-law exponent for the autocorrelation function is found to be $\lambda \approx 4.47$, violating an upper bound conjectured by Fisher and Huse.Comment: RevTex: 11 pages + 12 figures, submitted to PR

Bilayer Membrane in Confined Geometry: Interlayer Slide and Steric Repulsion

We derived free energy functional of a bilayer lipid membrane from the first principles of elasticity theory. The model explicitly includes position-dependent mutual slide of monolayers and bending deformation. Our free energy functional of liquid-crystalline membrane allows for incompressibility of the membrane and vanishing of the in-plane shear modulus and obeys reflectional and rotational symmetries of the flat bilayer. Interlayer slide at the mid-plane of the membrane results in local difference of surface densities of the monolayers. The slide amplitude directly enters free energy via the strain tensor. For small bending deformations the ratio between bending modulus and area compression coefficient, Kb/KA, is proportional to the square of monolayer thickness, h. Using the functional we performed self-consistent calculation of steric potential acting on bilayer between parallel confining walls separated by distance 2d. We found that temperature-dependent curvature at the minimum of confining potential is enhanced four times for a bilayer with slide as compared with a unit bilayer. We also calculate viscous modes of bilayer membrane between confining walls. Pure bending of the membrane is investigated, which is decoupled from area dilation at small amplitudes. Three sources of viscous dissipation are considered: water and membrane viscosities and interlayer drag. Dispersion has two branches. Confinement between the walls modifies the bending mode with respect to membrane in bulk solution. Simultaneously, inter-layer slipping mode, damped by viscous drag, remains unchanged by confinement.Comment: 23 pages,3 figures, pd

Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation

The Dice similarity coefficient (DSC) is both a widely used metric and loss function for biomedical image segmentation due to its robustness to class imbalance. However, it is well known that the DSC loss is poorly calibrated, resulting in overconfident predictions that cannot be usefully interpreted in biomedical and clinical practice. Performance is often the only metric used to evaluate segmentations produced by deep neural networks, and calibration is often neglected. However, calibration is important for translation into biomedical and clinical practice, providing crucial contextual information to model predictions for interpretation by scientists and clinicians. In this study, we provide a simple yet effective extension of the DSC loss, named the DSC++ loss, that selectively modulates the penalty associated with overconfident, incorrect predictions. As a standalone loss function, the DSC++ loss achieves significantly improved calibration over the conventional DSC loss across six well-validated open-source biomedical imaging datasets, including both 2D binary and 3D multi-class segmentation tasks. Similarly, we observe significantly improved calibration when integrating the DSC++ loss into four DSC-based loss functions. Finally, we use softmax thresholding to illustrate that well calibrated outputs enable tailoring of recall-precision bias, which is an important post-processing technique to adapt the model predictions to suit the biomedical or clinical task. The DSC++ loss overcomes the major limitation of the DSC loss, providing a suitable loss function for training deep learning segmentation models for use in biomedical and clinical practice. Source code is available at https://github.com/mlyg/DicePlusPlus

Enhancing the Financial Returns of R&D Investments through Operations Management

Although much research has been carried out to examine various contextual issues and moderating factors for successful R&D investments, very little research has been conducted to explore the role of a firm’s operational and process characteristics. In this study, we explore how firms could possibly enhance the financial returns of R&D investments through quality management, using Six Sigma implementation as an example, and efficiency improvement, using the stochastic frontier estimation of relative efficiency as a proxy. Based on data from 468 manufacturing firms in the U.S. over the period 2007-2014, we construct a dynamic panel data model to capture the effects of R&D investments on firms’ financial returns in terms of Tobin’s q. Using the system generalized method of moments estimator, our results indicate that the financial returns of R&D investments are significantly enhanced when firms adopt Six Sigma and improve efficiency in operations. Our additional analyses further suggest that such an enhancement effect through quality and efficiency improvements is more pronounced under high operational complexity as approximated by labor intensity and geographical diversity. Instead of considering innovation activities and process management as contradictory functions, we show that quality and efficiency improvements indeed support firms’ R&D investments, leading to higher financial returns

Viscosity effects on sand flow regimes and transport velocity in horizontal pipelines

Solids transport in multiphase systems is one of the issues under the umbrella of ‘‘flow assurance.’ But unlike issues such as waxes and hydrates, solids transport has received relatively little interest to date. The overall aim of this research was to investigate the fluid viscosity effects on sand particle transport characteristics in pipelines. Investigations were conducted using a 3-inch test facility for oil and a 4- inch flow loop for water and CMC experiments. Three oil viscosities were used including 105 cP, 200 cP and 340 cP. The sand used had a density of 2650 kg/m3 and a median diameter of 0.2 mm. The sand loadings were 50 lb/1000 bbl and 200lb/1000bbl. Based on the King et al (2000) sand minimum transport condition definition, the sand transport velocity for water, CMC solutions and oil (105 cP, 200 cP and 340 cP) were determined by visual observation and camera. The observed sand/oil flow regimes were compared. For oil/sand tests, it was observed that the dominant regime when approaching the critical sand transport velocity was the sliding sand bed, sand dunes were notably absent. However, for water and 7 cP CMC solution, sand dunes and sliding sand bed regimes were observed when approaching the sand transport velocity. For 20cP CMC solution, it was observed that the sand particles in the region between the main dunes were very active compared to those within the dunes