Model of correlated sequential adsorption of colloidal particles

We present results of a new model of sequential adsorption in which the adsorbing particles are correlated with the particles attached to the substrate. The strength of the correlations is measured by a tunable parameter $\sigma$. The model interpolates between free ballistic adsorption in the limit $\sigma\to\infty$ and a strongly correlated phase, appearing for $\sigma\to0$ and characterized by the emergence of highly ordered structures. The phenomenon is manifested through the analysis of several magnitudes, as the jamming limit and the particle-particle correlation function. The effect of correlations in one dimension manifests in the increased tendency to particle chaining in the substrate. In two dimensions the correlations induce a percolation transition, in which a spanning cluster of connected particles appears at a certain critical value $\sigma_c$. Our study could be applicable to more general situations in which the coupling between correlations and disorder is relevant, as for example, in the presence of strong interparticle interactions.Comment: 6 pages, 8 EPS figures. Phys. Rev. E (in press

Adsorption of colloidal particles in the presence of external field

We present a new class of sequential adsorption models in which the adsorbing particles reach the surface following an inclined direction (shadow models). Capillary electrophoresis, adsorption in the presence of a shear or on an inclined substrate are physical manifestations of these models. Numerical simulations are carried out to show how the new adsorption mechanisms are responsible for the formation of more ordered adsorbed layers and have important implications in the kinetics, in particular modifying the jamming limit.Comment: LaTex file, 3 figures available upon request, to appear in Phys.Rev.Let

(Mal)Adaptive Learning After Switches Between Object-Based and Rule-Based Environments

In reinforcement-learning studies, the environment is typically object-based; that is, objects are predictive of a reward. Recently, studies also adopted rule-based environments in which stimulus dimensions are predictive of a reward. In the current study, we investigated how people learned (1) in an object-based environment, (2) following a switch to a rule-based environment, (3) following a switch to a different rule-based environment, and (4) following a switch back to an object-based environment. To do so, we administered a reinforcement-learning task comprising of four blocks with consecutively an object-based environment, a rule-based environment, another rule-based environment, and an object-based environment. Computational-modeling results suggest that people (1) initially adopt rule-based learning despite its suboptimal nature in an object-based environment, (2) learn rules after a switch to a rule-based environment, (3) experience interference from previously-learned rules following a switch to a different rule-based environment, and (4) learn objects after a final switch to an object-based environment. These results imply people have a hard time adjusting to switches between object-based and rule-based environments, although they do learn to do so

Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass

Above ground biomass (AGB) is a parameter commonly used for assessment of grassland systems. Destructive AGB measurements, although accurate, are time consuming and are not easily undertaken on a repeat basis or over large areas. Structure-from-Motion (SfM) photogrammetry and Terrestrial Laser Scanning (TLS) are two technologies that have the potential to yield precise 3D structural measurements of vegetation quite rapidly. Recent advances have led to the successful application of TLS and SfM in woody biomass estimation, but application in natural grassland systems remains largely untested. The potential of these techniques for AGB estimation is examined considering 11 grass plots with a range of biomass in South Dakota, USA. Volume metrics extracted from the TLS and SfM 3D point clouds, and also conventional disc pasture meter settling heights, were compared to destructively harvested AGB total (grass and litter) and AGB grass plot measurements. Although the disc pasture meter was the most rapid method, it was less effective in AGB estimation (AGBgrass r2 = 0.42, AGBtotal r2 = 0.32) than the TLS (AGBgrass r2 = 0.46, AGBtotal r2 = 0.57) or SfM (AGBgrass r2 = 0.54, AGBtotal r2 = 0.72) which both demonstrated their utility for rapid AGB estimation of grass systems