Pattern formation during diffusion limited transformations in solids

We develop a description of diffusion limited growth in solid-solid transformations, which are strongly influenced by elastic effects. Density differences and structural transformations provoke stresses at interfaces, which affect the phase equilibrium conditions. We formulate equations for the interface kinetics similar to dendritic growth and study the growth of a stable phase from a metastable solid in both a channel geometry and in free space. We perform sharp interface calculations based on Green's function methods and phase field simulations, supplemented by analytical investigations. For pure dilatational transformations we find a single growing finger with symmetry breaking at higher driving forces, whereas for shear transformations the emergence of twin structures can be favorable. We predict the steady state shapes and propagation velocities, which can be higher than in conventional dendritic growth.Comment: submitted to Philosophical Magazin

Theory of dendritic growth in the presence of lattice strain

Elastic effects due to lattice strain modify the local equilibrium condition at the solid-solid interface compared to the classical dendritic growth. Both, the thermal and the elastic fields are eliminated by the Green's function techniques and a closed nonlinear integro-differential equation for the evolution of the interface is derived. In the case of pure dilatation, the elastic effects lead only to a trivial shift of the transition temperature while in the case of shear transitions, dendritic patterns are found even for isotropic surface energy

Machine learning techniques for MRI feature-based detection of frontotemporal lobar degeneration

Making a diagnosis of neurodegenerative diseases at an early stage is one of the most significant challenges of modern neuroscience. Although this family of diseases remains without a cure, the effectiveness of their medical treatment largely relies on the timing of their detection. For certain groups of diseases, such as Fronto-Temporal Dementia (FTD), trained professionals can effectively reach a correct diagnosis through the visual analysis of Magnetic Resonance Imaging, in its functional (fMRI) or raw (MRI) version. However, this operation is time-consuming and may be subject to personal interpretation. In this paper, we explore the performance of a group of machine learning algorithms to formulate a correct FTD diagnosis, in order to provide medical professionals with a supporting tool. The dataset consists of MRI data acquired on 30 subjects, and the experiments are carried out by investigating different fMRI techniques based on a Multi-Voxel Pattern Analysis (MVPA) approach. The results obtained show high accuracy in identifying FTD in elderly patients when Support Vector Machine and Random Forest techniques are used, with outcomes varying based on the fMRI methods

Nonlinear Two-Dimensional Green's Function in Smectics

The problem of the strain of smectics subjected to a force distributed over a line in the basal plane has been solved