The configuration evolution and macroscopic elasticity of fluid-filled closed cell composites : micromechanics and multiscale homogenization modelling

For fluid-filled closed cell composites widely distributed in nature, the configuration evolution and effective elastic properties are investigated using a micromechanical model and a multiscale homogenization theory, in which the effect of initial fluid pressure is considered. Based on the configuration evolution of the composite, we present a novel micromechanics model to examine the interactions between the initial fluid pressure and the macroscopic elasticity of the material. In this model, the initial fluid pressure of the closed cells and the corresponding configuration can be produced by applying an eigenstrain at the introduced fictitious stress-free configuration, and the pressure-induced initial microscopic strain is derived. Through a configuration analysis, we find the initial fluid pressure has a prominent effect on the effective elastic properties of freestanding materials containing pressurized fluid pores, and a new explicit expression of effective moduli is then given in terms of the initial fluid pressure. Meanwhile, the classical multiscale homogenization theory for calculating the effective moduli of a periodical heterogeneous material is generalized to include the pressurized fluid "inclusion" effect. Considering the coupling between matrix deformation and fluid pressure in closed cells, the multiscale homogenization method is utilized to numerically determine the macroscopic elastic properties of such composites at the unit cell level with specific boundary conditions. The present micromechanical model and multiscale homogenization method are illustrated by several numerical examples for validation purposes, and good agreements are achieved. The results show that the initial pressure of the fluid phase can strengthen overall effective bulk modulus but has no contribution to the shear modulus of fluid-filled closed cell composites.

Multiscale modelling of stress and strain partitioning in high strength dual phase steels

Multiscale modelling of stress and strain partitioning in DP steel was carried out using both realistic microstructure-based RVE models as well as stochastic microstructures generated by Monte Carlo method. The stochastic microstructure models were shown to resemble that of realistic microstructures, enabling research on the specific aspects of the microstructure that could be difficult to control and study during experimental work. One such feature of the realistic microstructures studied in this work was the grain size and microstructure morphology. The microstructures were generated with varying average grain sizes while all other parameters, such as boundary conditions, material properties and volume fractions of martensite and ferrite were kept constant. It is found that the effect of grain size is much more pronounced during the initial localisation of the plastic deformation at and around the interface of the phases. In addition, the decrease in ductility and increase in strength of the DP steels are directly related to the refinement of grain sizes of each phase and the stress-strain partitioning in between them.

Multiscale microstructure engineering of steels

The development of modern steels is based on the tailoring of the microstructure to achieve the required properties. While historically this was performed at the micrometre scale length, there is now the scope to undertake this at the nanoscale or atom scale. The present paper reviews recent work related to the development of ultrafine and nanoscale microstructures in steel as well as changes at shorter scale lengths, such as cluster formation and solute effects. This includes the development of ultrafine ferrite through phase transformation, nanoscale and ultrafine bainite, precipitation and cluster strengthening and bake hardening of steels. A key element of the present work has been the use of atom probe tomography to unlock the nature of these structures.

Integrating geo-referenced multiscale and multidisciplinary data for the management of biodiversity in livestock genetic resources

P>In livestock genetic resource conservation, decision making about conservation priorities is based on the simultaneous analysis of several different criteria that may contribute to long-term sustainable breeding conditions, such as genetic and demographic characteristics, environmental conditions, and role of the breed in the local or regional economy. Here we address methods to integrate different data sets and highlight problems related to interdisciplinary comparisons. Data integration is based on the use of geographic coordinates and Geographic Information Systems (GIS). In addition to technical problems related to projection systems, GIS have to face the challenging issue of the non homogeneous scale of their data sets. We give examples of the successful use of GIS for data integration and examine the risk of obtaining biased results when integrating datasets that have been captured at different scales.

Multiscale Fractal Descriptors and Polynomial Classifier for Partial Pixels Identification in Regions of Interest of Mammographic Images

Computer systems are used to support breast cancer diagnosis, with decisions taken from measurements carried out in regions of interest (ROIs). We show that support decisions obtained from square or rectangular ROIs can to include background regions with different behavior of healthy or diseased tissues. In this study, the background regions were identified as Partial Pixels (PP), obtained with a multilevel method of segmentation based on maximum entropy. The behaviors of healthy, diseased and partial tissues were quantified by fractal dimension and multiscale lacunarity, calculated through signatures of textures. The separability of groups was achieved using a polynomial classifier. The polynomials have powerful approximation properties as classifiers to treat characteristics linearly separable or not. This proposed method allowed quantifying the ROIs investigated and demonstrated that different behaviors are obtained, with distinctions of 90% for images obtained in the Cranio-caudal (CC) and Mediolateral Oblique (MLO) views.

Fractals de Rauzy

We study arithmetical and topological properties of a class of Rauzy Fractals. In particular, we give a parametrization of the boundaries of these sets and show that they are quasi-disks.

Tilings induced by a class of cubic Rauzy fractals

We study aperiodic and periodic tilings induced by the Rauzy fractal and its subtiles associated with beta-substitutions related to the polynomial x3-ax2-bx-1 for a≥b≥1. In particular, we compute the corresponding boundary graphs, describing the adjacencies in the tilings. These graphs are a valuable tool for more advanced studies of the topological properties of the Rauzy fractals. As an example, we show that the Rauzy fractals are not homeomorphic to a closed disc as soon as a≤2b-4. The methods presented in this paper may be used to obtain similar results for other classes of substitutions.© 2012 Elsevier B.V. All rights reserved.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic

In this study is presented an automatic method to classify images from fractal descriptors as decision rules, such as multiscale fractal dimension and lacunarity. The proposed methodology was divided in three steps: quantification of the regions of interest with fractal dimension and lacunarity, techniques under a multiscale approach; definition of reference patterns, which are the limits of each studied group; and, classification of each group, considering the combination of the reference patterns with signals maximization (an approach commonly considered in paraconsistent logic). The proposed method was used to classify histological prostatic images, aiming the diagnostic of prostate cancer. The accuracy levels were important, overcoming those obtained with Support Vector Machine (SVM) and Bestfirst Decicion Tree (BFTree) classifiers. The proposed approach allows recognize and classify patterns, offering the advantage of giving comprehensive results to the specialists.

A class of cubic Rauzy fractals

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)