The influences and outcomes of phonological awareness: a study of MA, PA and auditory processing in pre-readers with a family risk of dyslexia

The direct influence of phonological awareness (PA) on reading outcomes has been widely demonstrated, yet PA may also exert indirect influence on reading outcomes through other cognitive variables such as morphological awareness (MA). However, PA's own development is dependent and influenced by many extraneous variables such as auditory processing, which could ultimately impact reading outcomes. In a group of pre-reading children with a family risk of dyslexia and low-risk controls, this study sets out to answer questions surrounding PA's relationship at various grain sizes (syllable, onset/rime and phoneme) with measures of auditory processing (frequency modulation (FM) and an amplitude rise-time task (RT)) and MA, independent of reading experience. Group analysis revealed significant differences between high- and low-risk children on measures of MA, and PA at all grain sizes, while a trend for lower RT thresholds of high-risk children was found compared with controls. Correlational analysis demonstrated that MA is related to the composite PA score and syllable awareness. Group differences on MA and PA were re-examined including PA and MA, respectively, as control variables. Results exposed PA as a relevant component of MA, independent of reading experience

Modeling of evolving textures using granulometries

This chapter describes a statistical approach to classification of dynamic texture images, called parallel evolution functions (PEFs). Traditional classification methods predict texture class membership using comparisons with a finite set of predefined texture classes and identify the closest class. However, where texture images arise from a dynamic texture evolving over time, estimation of a time state in a continuous evolutionary process is required instead. The PEF approach does this using regression modeling techniques to predict time state. It is a flexible approach which may be based on any suitable image features. Many textures are well suited to a morphological analysis and the PEF approach uses image texture features derived from a granulometric analysis of the image. The method is illustrated using both simulated images of Boolean processes and real images of corrosion. The PEF approach has particular advantages for training sets containing limited numbers of observations, which is the case in many real world industrial inspection scenarios and for which other methods can fail or perform badly. [41] G.W. Horgan, Mathematical morphology for analysing soil structure from images, European Journal of Soil Science, vol. 49, pp. 161–173, 1998. [42] G.W. Horgan, C.A. Reid and C.A. Glasbey, Biological image processing and enhancement, Image Processing and Analysis, A Practical Approach, R. Baldock and J. Graham, eds., Oxford University Press, Oxford, UK, pp. 37–67, 2000. [43] B.B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the Making, A.K. Peters Ltd., Wellesley, MA, 1995. [44] H. Iversen and T. Lonnestad. An evaluation of stochastic models for analysis and synthesis of gray-scale texture, Pattern Recognition Letters, vol. 15, pp. 575–585, 1994. [45] A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, vol. 24(12), pp. 1167–1186, 1991. [46] T. Jossang and F. Feder, The fractal characterization of rough surfaces, Physica Scripta, vol. T44, pp. 9–14, 1992. [47] A.K. Katsaggelos and T. Chun-Jen, Iterative image restoration, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 208–209, 2000. [48] M. K¨oppen, C.H. Nowack and G. R¨osel, Pareto-morphology for color image processing, Proceedings of SCIA99, 11th Scandinavian Conference on Image Analysis 1, Kangerlussuaq, Greenland, pp. 195–202, 1999. [49] S. Krishnamachari and R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation, IEEE Transactions on Image Processing, vol. 6(2), pp. 251–267, 1997. [50] T. Kurita and N. Otsu, Texture classification by higher order local autocorrelation features, Proceedings of ACCV93, Asian Conference on Computer Vision, Osaka, pp. 175–178, 1993. [51] S.T. Kyvelidis, L. Lykouropoulos and N. Kouloumbi, Digital system for detecting, classifying, and fast retrieving corrosion generated defects, Journal of Coatings Technology, vol. 73(915), pp. 67–73, 2001. [52] Y. Liu, T. Zhao and J. Zhang, Learning multispectral texture features for cervical cancer detection, Proceedings of 2002 IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 169–172, 2002. [53] G. McGunnigle and M.J. Chantler, Modeling deposition of surface texture, Electronics Letters, vol. 37(12), pp. 749–750, 2001. [54] J. McKenzie, S. Marshall, A.J. Gray and E.R. Dougherty, Morphological texture analysis using the texture evolution function, International Journal of Pattern Recognition and Artificial Intelligence, vol. 17(2), pp. 167–185, 2003. [55] J. McKenzie, Classification of dynamically evolving textures using evolution functions, Ph.D. Thesis, University of Strathclyde, UK, 2004. [56] S.G. Mallat, Multiresolution approximations and wavelet orthonormal bases of L2(R), Transactions of the American Mathematical Society, vol. 315, pp. 69–87, 1989. [57] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989. [58] B.S. Manjunath and W.Y. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837–842, 1996. [59] B.S. Manjunath, G.M. Haley and W.Y. Ma, Multiband techniques for texture classification and segmentation, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 367–381, 2000. [60] G. Matheron, Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York, 1975

Understanding macroscale functionality of metal halide perovskites in terms of nanoscale heterogeneities

Hybrid metal halide perovskites have shown an unprecedented rise as semiconductor building blocks for solar energy conversion and light-emitting applications. Currently, the field moves empirically towards more and more complex chemical compositions, including mixed halide quadruple cation compounds that allow optical properties to be tuned and show promise for better stability. Despite tremendous progress in the field, there is a need for better understanding of mechanisms of efficiency loss and instabilities to facilitate rational optimization of composition. Starting from the device level and then diving into nanoscale properties, we highlight how structural and compositional heterogeneities affect macroscopic optoelectronic characteristics. Furthermore, we provide an overview of some of the advanced spectroscopy and imaging methods that are used to probe disorder and non-uniformities. A unique feature of hybrid halide perovskite compounds is the propensity for these heterogeneities to evolve in space and time under relatively mild illumination and applied electric fields, such as those found within active devices. This introduces an additional challenge for characterization and calls for application of complimentary probes that can aid in correlating the properties of local disorder with macroscopic function, with the ultimate goal of rationally tailoring synthesis towards optimal structures and compositions

Phase Stability and Segregation in Alloy 22 Base Metal and Weldments

The current design of the waste disposal containers relies heavily on encasement in a multi-layered container, featuring a corrosion barrier of Alloy 22, a Ni-Cr-Mo-W based alloy with excellent corrosion resistance over a wide range of conditions. The fundamental concern from the perspective of the Yucca Mountain Project, however, is the inherent uncertainty in the (very) long-term stability of the base metal and welds. Should the properties of the selected materials change over the long service life of the waste packages, it is conceivable that the desired performance characteristics (such as corrosion reistance) will become compromised, leading to premature failure of the system. To address this, we will study the phase stability and solute segregation characteristics of Alloy 22 base metal and welds. A better understanding of the underlying microstructural evolution tendencies, and their connections with corrosion behavior will (in turn) produce a higher confidence in the extrapolated behavior of the container materials over time periods that are not feasibly tested in a laboratory. Additionally, the knowledge gained here may potentially lead to cost savings through development of safe and realistic design constraints and model assumptions throughout the entire disposal system

Latest developments in mechanical properties and metallurgical features of high strength line pipe steels

In response to the increasing demand to improve both transportation efficiency and performance, the steel pipe industry has conducted extensive efforts to develop line pipe steel grades with superior metallurgical and mechanical (strength, toughness and ductility) properties in order to allow exploitation in hostile environments. This paper aims to give an overview of recent developments of high strength pipe steel grades as API 5L X70 and beyond, providing a detailed understanding of the continuous improvements with respect to a strain-based design context. Information regarding the metallurgy and processing, such as chemical composition, microstructural design, thermo-mechanical controlled process (TMCP) and accelerated cooling process (AcC), to achieve the target strength, ductility and toughness properties are discussed

Deformation and annealing study of Nicraly

Extensive experiments were carried out on the ODS alloy Nicraly, (an alloy prepared by mechanical alloying and consolidating a powder blend consisting of 16% chromium, 4% aluminum, 2-3% yttria, balance nickel), in efforts to develop methods of controlling the grain size and grain shape of the material. The experiments fell into two general categories: variations in the annealing parameters using the as-extruded material as it was received, and various thermomechanical processing schedules (various combinations of cold work and annealing). Success was achieved in gaining grain size and grain shape control by annealing of the as-extruded material. By proper selection of annealing temperature and cooling rates, the grain size of the as-received material was increased almost two orders of magnitude (from an average grain dimension of 0.023 mm to 1.668 mm) while the aspect ratio was increased by some 50% (from 20:1 to 30:1). No success was achieved in gaining significant control of the grain size and shape of the material by thermo-mechanical processing

High-temperature mechanical behavior of polycrystalline yttrium-doped barium cerate perovskite

The high-temperature mechanical properties of the mixed ionic-electronic conductor perovskite BaCe0.95Y0.05O3-δ with average grain size of 0.40μm have been studied in compression between 1100 and 1300°C in air at different initial strain rates. The true stress-true strain curves display an initial stress drop, followed by an extended steady-state stage. As the temperature decreases and/or the strain rate increases, there is a transition to a damage-tolerant strain-softening stage and eventually to catastrophic failure. Analysis of mechanical and microstructural data revealed that grain boundary sliding is the primary deformation mechanism. The strength drop has been correlated with the growth of ultrafine grains during deformation, already present at grain boundaries and triple grain junctions in the as-fabricated material.Ministerio de Ciencia e Innovación MAT2009-13979-C03-0