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Č,í,ž,ek, J.,Janeč,ek, M.,Krajň,á,k, T.,Strá,ská,, J.,Hruš,ka, P.,Gubicza, J.,Kim, H.S. Elsevier 2016 Acta materialia Vol.105 No.-
<P>Interstitial free steel with ultrafine-grained (UFG) structure was prepared by high-pressure torsion (HPT). The development of the microstructure as a function of the number of HPT turns was studied at the centre, half-radius and periphery of the HPT-processed disks by X-ray line profile analysis (XLPA), positron annihilation spectroscopy (PAS) and electron microscopy. The dislocation densities and the dislocation cell sizes determined by XLPA were found to be in good agreement with those obtained by PAS. The evolution of the dislocation density, the dislocation cell and grain sizes, the vacancy cluster size, as well as the high-angle grain boundary (HAGB) fraction was determined as a function of the equivalent strain. It was found that first the dislocation density saturated, then the dislocation cell size reached its minimum value and finally the grain size got saturated. For very high strains after the saturation of grain size the HAGB fraction further increased. The PAS investigations revealed that vacancies introduced by severe plastic deformation agglomerated into small clusters consisting of 9-14 vacancies. The evolution of the yield strength calculated from the microhardness as a function of strain was explained by the development of the defect structure. (C) 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.</P>
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Incorporation of spatial autocorrelation improves soil–landform modeling at A and B horizons
Kim, Daehyun,Š,amonil, Pavel,Jeong, Gwanyong,Tejnecký,, Vá,clav,Drá,bek, Ondř,ej,Hruš,ka, Jakub,Park, Soo Jin Catena Verlag 2019 Catena Vol.183 No.-
<P><B>Abstract</B></P> <P>Research has shown that the performance of soil–landform models would improve if the effects of spatial autocorrelation were properly accounted for; however, it remains elusive whether the level of improvement would be predictable, based on the degree of spatial autocorrelation in the model variables. We evaluated this problem using 11 soil variables acquired from the A and B horizons along a hillslope of Žofínský Prales in the Czech Republic. The results showed that, with no exception, there were increases in R<SUP>2</SUP> and decreases in the Akaike information criterion (AIC), residual autocorrelation, and root-mean-square errors (RMSEs), after incorporating the spatial filters extracted by spatial eigenvector mapping into non-spatial regression models. Furthermore, the improvement of the model was positively proportional to the degree of spatial autocorrelation, inherent in the soil variables. That is, there were strikingly linear and significant relationships, in which strongly autocorrelated soil variables (i.e., having a high Moran's <I>I</I> value) exhibited greater increases in R<SUP>2</SUP> and decreases in AIC, residual autocorrelation, and RMSEs than their more weakly autocorrelated counterparts. These findings indicate that the degree of spatial autocorrelation present in soil properties can serve as a direct indicator for how much the performance of a traditional non-spatial soil–landform model would be enhanced, by explicitly taking into consideration the presence of spatial autocorrelation. More generally, our results potentially imply that the need for and benefit from incorporating spatial effects in geopedological modeling proportionally increases as the soil property of interest is more spatially structured (i.e., landform variables alone cannot capture soil spatial variability).</P> <P><B>Highlights</B></P> <P> <UL> <LI> Soil spatial variability was modeled using landform variables in a temperate forest. </LI> <LI> In this regression, we included spatial filters as additional independent variables. </LI> <LI> This inclusion improved the performance of the original non-spatial approach. </LI> <LI> Spatial autocorrelation of soil variables predicted the degree of such improvement. </LI> </UL> </P>
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