The influence of stacking fault energy on ductile fracture micromorphology

The influence of stacking fault energy on microvoid coalescence in pure materials has been studied. It was shown that as a material's stacking fault energy (SFE) decreased, the extent of microvoid coalescence that occurred during ductile fracture also decreased. The decrease of microvoid coalescence in low SFE materials was attributed to a hindrance in the development of dislocation cells associated with the restricted motion of dislocations. In pure materials, microvoids are believed to initiate and grow along dislocation cell walls formed during deformation. As such, the absence or scarcity of cells in lower SFE materials limits the formation of these voids during ductile fracture.     

Effect of test frequency and water content on localized crack-tip heating in nylon-6,6

Fatigue crack propagation (FCP) tests were conducted at various frequencies on nylon-6,6 specimens equilibrated over a range of moisture levels to determine the crack growth rates and the crack-tip temperatures as a function of water content. Frequency-sensitivity was correlated with the amount of crack-tip heating taking place, with crack-tip temperature being found to depend strongly on the estimated loss compliance, D″, of the material. The frequency-sensitivity of FCP in nylon was seen also to be affected by mean stress, suggesting that creep processes are often significant in FCP of nylon.     

Structure-property relationships in rubber-toughened epoxies

Epoxies toughened with two reactive liquid rubbers, an epoxy-terminated butadiene acrylonitrile rubber (ETBN) and an amino-terminated butadiene acrylonitrile rubber (ATBN), were prepared and studied in terms of their structure property relationships. A two-phase structure was formed, consisting of spherical rubber particles dispersed in an epoxy matrix. A broad distribution of rubber particles was observed in all the materials with most of the particles ranging in size from 1 to 4 μm, but some particles exceeding 20 μm were also found. Impact strength, plane strain fracture toughness (K_IC), and fracture energy (G_IC) were increased, while Young's modulus and yield strength decreased slightly with increasing rubber content and volume fraction of the dispersed phase. Both G_IC and K_IC were found to increase with increasing apparent molecular weight between crosslinks and decreasing yield strength. The increased size of the plastic zone at the crack tip associated with decreasing yield strength could be the cause of the increased toughness. An ATBN-toughened system containing the greatest amount of epoxy sub-inclusion in the rubbery phase demonstrated the best fracture toughness in this series. In the present systems, rubber-enhanced shear deformation of the matrix is considered to be the major toughening mechanism. Curing conditions and the miscibility between the liquid rubber and the epoxy resin determine the phase morphology of the resulting two-phase systems. Kerner's equation successfully describes the modulus dependence on volume fraction for the two-phase epoxy materials.     

Constrained regression in chemical engineering practice

Absolute equality constraints in multiple regression are shown to introduce only minor modifications in the normal equations by a subset of the regression parameters. It is essential that these are ordered such that the subset is observable through the constraints. The constraints are shown to give greater residuals about the regression, but the precision in the parameter estimation is improved.In general power function curve fitting, a quadratically convergent iterative computation of the optimal exponents uses the normal equations as equality constraints. The correlations between the coefficients and the exponents in a sum of power functions become very high, when the number of terms increases such that their individual observability is being greatly reduced. The effect is shown to be a very poor precision in the parameter estimation and a heavy oscillation in the coefficients.The algorithms suggested are illustrated by curve fitting of some binary equilibrium data, where the relative volatility model gives a superior fit to the data.     

The stability and unfolding of an IgG binding protein based upon the B domain of protein A from Staphylococcus aureus probed by tryptophan substitution and fluorescence spectroscopy

The stability and unfolding of an immunoglobulin (Ig) G bindingprotein based upon the B domain of protein A (SpAB) from Staphylococcusaureus were studied by substituting tryptophan residues at strategiclocations within each of the three a-helical regions (al-a3)of the domain. The role of the C-terminal helix, a3, was investigatedby generating two protein constructs, one corresponding to thecomplete SpAB, the other lacking a part of ct3; the Trp substitutionswere made in both one-and two-domain versions of each of theseconstructs. The fluorescence properties of each of the single-tryptophanmutants were studied in the native state and as a function ofguanidine-HCl-mediated unfolding, and their IgG binding activitieswere determined by a competitive enzyme-linked immunosorbentassay. The free energies of folding and of binding to IgG foreach mutant were compared with those for the native domains.The effect of each substitution upon the overall structure andupon the IgG binding interface was modelled by molecular graphicsand energy minimization. These studies indicate that (i) 3 contributesto the overall stability of the domain and to the formationof the IgG binding site in l and 2, and (ii) al unfolds first,followed by 2 and 3 together.     

Robustness of ISS systems to inputs with limited moving average: Application to spacecraft formations

We provide a theoretical framework that fits realistic challenges related to spacecraft formation with disturbances. We show that the input‐to‐state stability of such systems guarantees some robustness with respect to a class of signals with bounded average‐energy, which encompasses the typical disturbances acting on spacecraft formations. Solutions are shown to converge to the desired formation, up to an offset, which is somewhat proportional to the considered moving average of disturbances. In the presence of fast peaking perturbations, the approach provides a tighter evaluation of the disturbances' influence, which allows for the use of more parsimonious control gains. Copyright © 2015 John Wiley & Sons, Ltd.     

Learning traversability models for autonomous mobile vehicles

Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the geometry and the appearance of the scene. The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is presented.

A new document representation using term frequency and vectorized graph connectionists with application to document retrieval

This paper presents a new document representation with vectorized multiple features including term frequency and term-connection-frequency. A document is represented by undirected and directed graph, respectively. Then terms and vectorized graph connectionists are extracted from the graphs by employing several feature extraction methods. This hybrid document feature representation more accurately reflects the underlying semantics that are difficult to achieve from the currently used term histograms, and it facilitates the matching of complex graph. In application level, we develop a document retrieval system based on self-organizing map (SOM) to speed up the retrieval process. We perform extensive experimental verification, and the results suggest that the proposed method is computationally efficient and accurate for document retrieval.     

Prediction of the binding mode of biarylpropylsulfonamide allosteric AMPA receptor modulators based on docking, GRID molecular interaction fields and 3D-QSAR analysis

A novel approach of combining flexible molecular docking, GRID molecular interaction fields, analysis of ligand-protein hydrogen bond interactions, conformational energy penalties and 3D-QSAR analysis was used to propose a binding mode in the dimer interface of the iGluR2 receptor for the biarylpropylsulfonamide class of positive allosteric AMPA modulators. Possible binding poses were generated by flexible molecular docking. GRID molecular interaction fields of the binding site, ligand-protein hydrogen bonding interactions and conformational energy penalties were used to select the most likely binding mode. The selected binding poses were subjected to a 3D-QSAR analysis using previously published activity data. The resulting model (2 LVs, R2=0.89, q2=0.61) predicted the activities of the compounds in the test set with a standard deviation on error of prediction of 0.17. The proposed binding mode was validated by interpretation of the PLS-coefficient regions from the 3D-QSAR analysis in terms of interactions between the receptor and the modulators.