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The conversion of native forest into agricultural land, which is common in many parts of the world, poses important questions regarding soil degradation, demanding further efforts to better understand the effect of land use change on soil functions. With the advent of 3D computed tomography techniques and computing power, new methods are becoming available to address this question. In this direction, in the current work we implement a modification of the Fisher–Shannon method, borrowed from information theory, to quantify the complexity of twelve 3D CT soil samples from a sugarcane plantation and twelve samples from a nearby native Atlantic forest in northeastern Brazil. The distinction found between the samples from the sugar plantation and the Atlantic forest site is quite pronounced. The results at the level of 91.7% accuracy were obtained considering the complexity in the Fisher–Shannon plane. Atlantic forest samples are found to be generally more complex than those from the sugar plantation.

Distance metric learning has attracted a lot of interest for solving machine learning and pattern recognition problems over the last decades. In this work we present a simple approach based on concepts from statistical physics to learn optimal distance metric for a given problem. We formulate the task as a typical statistical physics problem: distances between patterns represent constituents of a physical system and the objective function corresponds to energy. Then we express the problem as a minimization of the free energy of a complex system, which is equivalent to distance metric learning. Much like for many problems in physics, we propose an approach based on Metropolis Monte Carlo to find the best distance metric. This provides a natural way to learn the distance metric, where the learning process can be intuitively seen as stretching and rotating the metric space until some heuristic is satisfied. Our proposed method can handle a wide variety of constraints including those with spurious local minima. The approach works surprisingly well with stochastic nearest neighbors from neighborhood component analysis (NCA). Experimental results on artificial and real-world data sets reveal a clear superiority over a number of state-of-the-art distance metric learning methods for nearest neighbors classification.

To improve the understanding of the microscopic properties of the silicon oxidation process, we perform numerical simulations of thermal SiO2 thin film growth. Therefrom, we analyze the growth kinetics as well as the evolution of the SiO2/Si interface formation. The oxidation rate in the early stages of growth is governed by two processes : the rapid initial formation of the oxidation front, and its subsequent diffusion. The corresponding oxidation kinetics, arising from the simulations, provides a rather good description of a large variety of experimental data, with the minimum number of variable parameters. The effect of external parameters, such as temperature and pressure, can be explained in terms of scaling concepts. It is found that the temporal evolution of SiO2 concentration at a given fixed crystalline cross section is continuous, while the spatial distribution at any given time shows abrupt (step like) behavior, decaying from 100 % to zero concentration within just a few lattice constants. The results of the simulations suggest a very simple phenomenological approach for modeling of microstructural aspects of the SiO2/Si system

To improve the understanding of the microscopic properties of the silicon oxidation process, we perform numerical simulations of thermal SiO2 thin film growth. Therefrom, we analyze the growth kinetics as well as the evolution of the SiO2/Si interface formation. The oxidation rate in the early stages of growth is governed by two processes : the rapid initial formation of the oxidation front, and its subsequent diffusion. The corresponding oxidation kinetics, arising from the simulations, provides a rather good description of a large variety of experimental data, with the minimum number of variable parameters. The effect of external parameters, such as temperature and pressure, can be explained in terms of scaling concepts. It is found that the temporal evolution of SiO2 concentration at a given fixed crystalline cross section is continuous, while the spatial distribution at any given time shows abrupt (step like) behavior, decaying from 100 % to zero concentration within just a few lattice constants. The results of the simulations suggest a very simple phenomenological approach for modeling of microstructural aspects of the SiO2/Si system