Multifractal analysis of discretized X-ray CT images for the characterization of soil macropore structures

A correct statistical model of soil pore structure can be critical for understanding flow and transport processes in soils, and creating synthetic soil pore spaces for hypothetical and model testing, and evaluating similarity of pore spaces of different soils. Advanced visualization techniques such as X-ray computed tomography (CT) offer new opportunities of exploring heterogeneity of soil properties at horizon or aggregate scales. Simple fractal models such as fractional Brownian motion that have been proposed to capture the complex behavior of soil spatial variation at field scale rarely simulate irregularity patterns displayed by spatial series of soil properties. The objective of this work was to use CT data to test the hypothesis that soil pore structure at the horizon scale may be represented by multifractal models. X-ray CT scans of twelve, water-saturated, 20-cm long soil columns with diameters of 7.5 cm were analyzed. A reconstruction algorithm was applied to convert the X-ray CT data into a stack of 1480 grayscale digital images with a voxel resolution of 110 microns and a cross-sectional size of 690 × 690 pixels. The images were binarized and the spatial series of the percentage of void space vs. depth was analyzed to evaluate the applicability of the multifractal model. The series of depth-dependent macroporosity values exhibited a well-defined multifractal structure that was revealed by singularity and Rényi spectra. The long-range dependencies in these series were parameterized by the Hurst exponent. Values of the Hurst exponent close to one were observed indicating the strong persistence in variations of porosity with depth. The multifractal modeling of soil macropore structure can be an efficient method for parameterizing and simulating the vertical spatial heterogeneity of soil pore space

Modelling Multi-Scale Atmosphere And Land-Surface Interactions-A Large-Ensemble Approach-

The solid earth as a basic component of the climate system profoundly influences the development of the atmospheric boundary layer, in particular through processes at the interface. As land-surface properties are heterogeneous over a broad range of length-scales, surface-induced fluxes are heterogeneous too. Representing land-surface heterogeneity and the corresponding fluxes is a challenging task in numerical prediction of weather and projection of climate. Earlier studies separate the role of heterogeneity into flux aggregation and dynamic effects. In this work, we introduce the approach of 'para-real' ensemble modelling to investigate the dynamic effect of land-surface heterogeneity. We perform a large ensemble of high-resolution simulations using the Weather research and forecast model (WRF) in its advanced research mode (WRF-ARW) together with the Noah-MP land surface model (LSM). The para-real simulation ensembles are externally forced by a reanalysis of a real case in spring 2013, but become exposed to different synthesized surface patterns (SP) generated as quasi-fractal Brownian surfaces (quasi fBs) with exact control of the dominant wave length and fractal persistence to satisfy a tailored randomized-spectrum. The focus of this study is on the three inter-related land-surface and atmosphere coupling mechanisms--the thermodynamic coupling, aerodynamic coupling, and hydrological coupling. For each mechanism, a corresponding surface property is identified, namely surface albedo (α) for thermodynamic coupling, roughness length (z0) for aerodynamic coupling, and soil type (st) for hydrological coupling. For each surface property, we generate a set of quasi-fBs with different dominant length scale and fractal persistence. In our para-real ensembles, the original fields of the surface properties are--in a first step--derived from satellite data (for α) and/or in-situ estimates (for z0 and st). In a second step, these are replaced by the quasi-fBs, for which we estimate the control parameters from the original data, i.e., the probability density distribution of the original data matches that of the quasi-fBs which eliminates the flux aggregation effect and allows us to focus on the dynamic effect. In total, 480 simulations, i.e., ensembles of 48 physical cases each containing 10 random realizations, are analyzed using Analysis of Variance (ANOVA); this allows for an isolated analysis of the signal contained in particular dimensional combinations, for instance the horizontal plane. We find, first, a strong impact of the length scale of the surface forcing on the intensity of coupling: while the dynamic effect of surface heterogeneity significantly impacts the state of the atmospheric boundary layer for all cases investigated, the impact of the surface signal on the atmospheric state grows with the length-scale of the surface heterogeneity. Second, we demonstrate that larger fractal persistence of the surface signal also strengthens the atmosphere--surface coupling. Third, the qualitative impact of the surface forcing is shown to depend on time, which eliminates the possibility of a simple linear forward propagation of the surface signal; there is strong sensitivity to the diurnal cycle, in particular with respect to the horizontal wind components: The maximum intensity of atmosphere--surface coupling (measured in terms of correlation) is found around noon for the atmospheric temperature, and some hours later (in the early afternoon) for water vapor. Fourth, among the different surface forcing investigated, we find that the heterogeneity of soil type is the most important to the atmospheric state--surface exchanges and its signal are detected in the atmospheric water-vapor up to 2km height; in particular, the soil-type pattern with the smallest length-scale causes a doubling of cloud-water above 500m height whereas no impact on the bulk atmospheric state is found for patterns with other length-scales and fractal persistence or forcing of other surface variables. This illustrates the key part that hydrological coupling plays in connecting the atmosphere to the surface, and it underlines the relevance of improved hydrological process-level representation for improved parameterization of the coupled land--atmosphere system

Spatial models of metapopulations and benthic communities in patchy environments

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2000The distribution of organisms in space has important consequences for the function and structure of ecological systems. Such distributions are often referred to as patchy, and a patch-based approach to modeling ecosystem dynamics has become a major research focus. These models have been used to explore a wide range of questions concerning population, metapopulation, community, and landscape ecology, in both terrestrial and aquatic systems. In this dissertation I develop and analyze a series of spatial models to study the dynamics of metapopulations and marine benthic communities in patchy environments. All the models have the form of a discrete-time Markov chain, and assume that the landscape is composed of discrete patches, each of which is in one of a number of possible states. The state of a patch is determined by the presence of an individual of a given species, a local population, or a group of species, depending on the spatial scale of the model. The research is organized into two main parts as follows. In the first part, I present an analysis of the effects of habitat destruction on metapopulation persistence. Theoretical studies have already shown that a metapopulation goes extinct when the fraction of suitable patches in the landscape falls below a critical threshold (the so called extinction threshold). This result has become a paradigm in conservation biology and several models have been developed to calculate extinction thresholds for endangered species. These models, however, generally do not take into account the spatial arrangement of habitat destruction, or the actual size of the landscape. To investigate how the spatial structure of habitat destruction affects persistence, I compare the behavior of two models: a spatially implicit patch-occupancy model (which recreates the extinction patterns found in other models) and a spatially explicit cellular automaton (CA) model. In the CA, I use fractal arrangements of suitable and unsuitable patches to simulate habitat destruction and show that the extinction threshold depends on the fractal dimension of the landscape. To investigate how habitat destruction affects persistence in finite landscapes , I develop and analyze a chain-binomial metapopulation (CBM) model. This model predicts the expected extinction time of a metapopulation as a function of the number of patches in the landscape and the number of those patches that are suitable for the population. The CBM model shows that the expected time to extinction decreases greater than exponentially as suitable patches are destroyed. I also describe a statistical method for estimating parameters for the CBM model in order to evaluate metapopulation viability in real landscapes. In the second part, I develop and analyze a series of Markov chain models for a rocky subtidal community in the Gulf of Maine. Data for the model comes from ten permanent quadrats (located on Ammen Rock Pinnacle at 30 meters depth) monitored over an 8-year period (1986-1994). I first parameterize a linear (homogenous) Markov chain model from the data set and analyze it using an array of novel techniques, including a compression algorithm to classify species into functional groups, a set of measures from stochastic process theory to characterize successional patterns, sensitivity analyses to predict how changes in various ecological processes effect community composition, and a method for simulating species removal to identify keystone species. I then explore the effects of time and space on successional patterns using log-linear analysis, and show that transition probabilities vary significantly across small spatial scales and over yearly time intervals. I examine the implications of these findings for predicting equilibrium species abundances and for characterizing the transient dynamics of the community. Finally, I develop a nonlinear Markov chain for the rocky subtidal community. The model is parameterized using maximum likelihood methods to estimate density-dependent transition probabilities. I analyze the best fitting models to study the effects of nonlinear species interactions on community dynamics, and to identify multiple stable states in the subtidal system.This work was supported by the Office of Naval Research and the National Science Foundation through the following grants to Hal Caswell: ONR-URIP Grant NOOOl492- J-1527, NSF Grants DEB-9119420, DEB-95-27400, OCE-981267 and OCE-9302238

Natural Parameterization

The objective of this project has been to develop an approach for imitating physical objects with an underlying stochastic variation. The key assumption is that a set of “natural parameters” can be extracted by a new subdivision algorithm so they reflect what is called the object’s “geometric DNA”. A case study on one hundred wheat grain crosssections (Triticum aestivum) showed that it was possible to extract thirty-six such parameters and to reuse them for Monte Carlo simulation of “new” stochastic phantoms which possessthe same stochastic behavior as the “original” cross-sections

Interpretable deep learning for guided microstructure-property explorations in photovoltaics

The microstructure determines the photovoltaic performance of a thin film organic semiconductor film. The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate, thus making microstructure optimization challenging. Here, we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural networks. We characterize this approach in terms of two critical metrics, its generalizability (has it learnt a reasonable map?), and its intepretability (can it produce meaningful microstructure characteristics that influence its prediction?). A surrogate model that exhibits these two features of generalizability and intepretability is particularly useful for subsequent design exploration. We illustrate this by using the surrogate model for both manual exploration (that verifies known domain insight) as well as automated microstructure optimization. We envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems

Pore-Scale Modeling of the Surface Roughness Effect on Fluid-Fluid Interfacial Area for Contaminant Transport in Vadose Zone

Interaction between solid, wetting fluids and nonwetting fluids frequently occurs in natural environmental processes. An ongoing concern for researchers is the fluid-fluid interfaces on rough solid surfaces inside natural porous media. Interfacial area between the immiscible fluids could be greatly affected by grain surface roughness, in which the adsorbed wetting-fluid films serve as the critical intermediary. It has been demonstrated that the configuration of wetting films is a combination of two competitive surface forces: DLVO adsorption and capillarity, whose effects on wetting fluids can lead to significant changes in the shape of films, and thus distinctive film area under different matric potentials. Therefore, the methodology of the research is to characterize the mechanism of surface roughness involved in the configuration of wetting film, and the resultant change of film area, with an explicit quantitative model. The main body of the present modeling approach is to use a bundle-of-cylindrical-capillaries (BCC) model for pore geometry that is modified with a surface roughness factor based on the solid surface area. Film-associated interfacial area in the model is represented by an interfacial area factor normalized with solid surface roughness, which is quantified by an explicit sigmoid function (logistic function) that defines the change of film area within the range of two limiting conditions: smooth-surface and maximum roughness. For a given porous medium, its inherent solid phase properties, especially the fractal-scale microstructures of surface roughness, will generate a characteristic profile of interfacial-area vs. wetting-fluid saturation, which can be fitted from measured data from interfacial partitioning tracer tests (IPTT). Following the development of modeling approach, simulations with both pre-determined input parameters and actual experimental data were conducted. Example calculations and sensitivity analyses of critical model parameters revealed the phenomenon of “surface roughness masking” that occurred in the interfacial-area vs. saturation curves. Simulation test on experimental data sets for multiple porous media demonstrated the excellent performance of the modeling approach, in which each medium can be explicitly quantified with five critical modeling parameters—two for pore size distribution, one for the sample-scale surface roughness, and two for micro-scale roughness. Inspection of the relationship between roughness-related parameters showed that the micro-scale surface roughness of natural porous media only partially correlate to soil texture. Studies on images from scanning electron microscopy (SEM) also illustrated the complexity of surface roughness. The complicated nature of the micro-scale surface roughness highlighted the potential of the proposed methodology in various environmental applications. It would be particularly useful for systems that comprise large magnitudes of interfacial domain, with energy or mass transport between solid, fluid, and atmosphere

Advanced photonic and electronic systems WILGA 2018

WILGA annual symposium on advanced photonic and electronic systems has been organized by young scientist for young scientists since two decades. It traditionally gathers around 400 young researchers and their tutors. Ph.D students and graduates present their recent achievements during well attended oral sessions. Wilga is a very good digest of Ph.D. works carried out at technical universities in electronics and photonics, as well as information sciences throughout Poland and some neighboring countries. Publishing patronage over Wilga keep Elektronika technical journal by SEP, IJET and Proceedings of SPIE. The latter world editorial series publishes annually more than 200 papers from Wilga. Wilga 2018 was the XLII edition of this meeting. The following topical tracks were distinguished: photonics, electronics, information technologies and system research. The article is a digest of some chosen works presented during Wilga 2018 symposium. WILGA 2017 works were published in Proc. SPIE vol.10445. WILGA 2018 works were published in Proc. SPIE vol.10808