Tilted boxes on inclined planes

We propose the study of a box placed on an inclined plane, with an initial tilt with respect to the plane. This is a paradigmatic example of the role played by friction as a link between translational and rotational motion. This example has two advantages over the usual example of a sphere (or cylinder) rolling down an inclined plane. First, it provides a good model for a much greater variety of "real-life" situations. Second, it exhibits a much richer structure in parameter space, even when the box starts from rest. (C) 2000 American Association of Physics Teachers.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

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Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

Biomass production of four Cynara cardunculus clones and lignin composition analysis

Four Cynara cardunculus clones, two from Portugal and two from Spain were studied for biomass production and their lignin was characterized. The clones differed in biomass partitioning: Spanish clones produced more capitula (54.5% vs. 43.9%), and Portuguese clones more stalks (37.2% vs. 25.6%). The heating values (HHV0) of the stalks were similar, ranging from 17.1 to 18.4 MJ/kg. Lignin was studied by analytical pyrolysis (Py-GC/MS(FID)), separately in depithed stalks (stalksDP) and pith. StalksDP had in average higher relative proportions of lignin derived compounds than pith (23.9% vs. 21.8%) with slightly different lignin monomeric composition: pith samples were richer in syringyl units as compared to stalksDP (64% vs. 53%), with S/G ratios of 2.1 and 1.3, respectively. The H:G:S composition was 7:40:53 in stalksDP and 7:29:64 in pith. The lignin content ranged from 18.8% to 25.5%, enabling a differentiation between clones and provenances. © 2015 Elsevier Ltd. All rights reserved.

Effect of explosive mixture on quality of explosive welds of copper to aluminium

The aim of this research is to investigate the influence of explosive ratio and type of sensitizer on the quality of explosive welds between copper and aluminium alloy plates. The welds were performed on a partially overlapping joint configuration using an emulsion explosive (EE) with two different sensitizers, hollow glass microspheres (HGMS) and expanded polystyrene spheres (EPS). Welds with an improved surface were achieved by using the HGMS sensitizer. A higher wave amplitude was registered in welds produced with the EPS sensitizer. In turn, the dimension of the molten pockets was influenced by the explosive ratio, increasing in size with increases in the values of this parameter. The intermetallic content of these zones varied according to the sensitizer type. Unlike the CuAl2 phase, the Cu-richer phases CuAl and Cu9Al4 were only identified in welds performed using the EPS sensitizer. An increase in hardness was observed at the interface of all welds, which resulted from both the presence of intermetallic phases and the plastic deformation of the materials promoted by the impact. This effect was most evident on the aluminium alloy side. All the welds had a greater strength than copper, i.e. the weakest material of the joint. (C) 2016 Elsevier Ltd. All rights reserved.