Adsorption of frog foam nest proteins at the air-water interface

The surfactant properties of aqueous protein mixtures ( ranaspumins) from the foam nests of the tropical frog Physalaemus pustulosus have been investigated by surface tension, two-photon excitation. uorescence microscopy, specular neutron reflection, and related biophysical techniques. Ranaspumins lower the surface tension of water more rapidly and more effectively than standard globular proteins under similar conditions. Two- photon excitation. uorescence microscopy of nest foams treated with fluorescent marker ( anilinonaphthalene sulfonic acid) shows partitioning of hydrophobic proteins into the air-water interface and allows imaging of the foam structure. The surface excess of the adsorbed protein layers, determined from measurements of neutron reflection from the surface of water utilizing H2O/D2O mixtures, shows a persistent increase of surface excess and layer thickness with bulk concentration. At the highest concentration studied ( 0.5 mg ml(-1)), the adsorbed layer is characterized by three distinct regions: a protruding top layer of similar to20 Angstrom, a middle layer of similar to30 Angstrom, and a more diffuse submerged layer projecting some 25 Angstrom into bulk solution. This suggests a model involving self-assembly of protein aggregates at the air-water interface in which initial foam formation is facilitated by specific surfactant proteins in the mixture, further stabilized by subsequent aggregation and cross-linking into a multilayer surface complex.

Error estimates for a fully discrete spectral scheme for a class of nonlinear, nonlocal dispersive wave equations

We analyze a fully discrete spectral method for the numerical solution of the initial- and periodic boundary-value problem for two nonlinear, nonlocal, dispersive wave equations, the Benjamin–Ono and the Intermediate Long Wave equations. The equations are discretized in space by the standard Fourier–Galerkin spectral method and in time by the explicit leap-frog scheme. For the resulting fully discrete, conditionally stable scheme we prove an L2-error bound of spectral accuracy in space and of second-order accuracy in time.

The composite-tendency Robert-Asselin-Williams (RAW) filter in semi-implicit integrations

Timediscretization in weatherandclimate modelsintroduces truncation errors that limit the accuracy of the simulations. Recent work has yielded a method for reducing the amplitude errors in leap-frog integrations from first-order to fifth-order.This improvement is achieved by replacing the Robert–Asselin filter with the Robert–Asselin–Williams (RAW) filter and using a linear combination of unfiltered and filtered states to compute the tendency term. The purpose of the present article is to apply the composite-tendency RAW-filtered leapfrog scheme to semi-implicit integrations. A theoretical analysis shows that the stability and accuracy are unaffected by the introduction of the implicitly treated mode. The scheme is tested in semi-implicit numerical integrations in both a simple nonlinear stiff system and a medium-complexity atmospheric general circulation model and yields substantial improvements in both cases. We conclude that the composite-tendency RAW-filtered leap-frog scheme is suitable for use in semi-implicit integrations.

Efficiency of evolutionary algorithms in water network pipe sizing

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems.