Quantile estimation using auxiliary information with applications to soil texture data

In the Major Land Resource Area (MLRA) 107 pilot project, a multi-phase probability sampling design for updating soil surveys was implemented in western Iowa. In general, multi-phase designs are used when a variable of interest is expensive to measure, but is strongly related to another (auxiliary) variable which is inexpensive to observe. In a multi-phase design, the auxiliary variable is observed for a sample and the study variable is observed for a relatively small sub-sample. In the estimation stage, the auxiliary information is used to improve estimators of distributional quantities relating to the study variable. In particular, we consider estimation of quantiles in this context;Chambers and Dunstan (1986) (CD) presented an estimator for a finite population distribution function which incorporates auxiliary information. A linear relationship between the study variable and the auxiliary information is assumed. The residuals in the linear model are assumed to be homoskedastic. We derive a Bahadur-like representation for the quantile estimator corresponding to the CD distribution function estimator. This expression is used to derive an expression for the asymptotic variance of the quantile estimator;We consider estimation of quantiles for soil texture profiles using data from the MLRA 107 pilot project. The laboratory determination of soil texture is the variable of interest. Auxiliary information is available in the form of field determinations of soil texture. Due to the multi-phase sampling design used for data collection, field determinations are available at more sites than laboratory determinations. The CD quantile estimator is modified to incorporate sampling weights and to allow heteroskedasticity in the assumed linear model;A Bayesian approach to this estimation problem is also considered. A hierarchical model is used to describe the relationships between observed data and unknown parameters. Soil horizon profiles are modeled as realizations of Markov chains. Transformed textures are modeled with Gaussian mixtures. The posterior distribution of soil texture profiles is numerically approximated using a Gibbs sampler. The hierarchical model provides a comprehensive framework which may be useful for analyzing other variables collected in the pilot project. The two approaches are compared using simulated and real data

Computer simulations of realistic three-dimensional microstructures

A novel and efficient methodology is developed for computer simulations of realistic two-dimensional (2D) and three-dimensional (3D) microstructures. The simulations incorporate realistic 2D and 3D complex morphologies/shapes, spatial patterns, anisotropy, volume fractions, and size distributions of the microstructural features statistically similar to those in the corresponding real microstructures. The methodology permits simulations of sufficiently large 2D as well as 3D microstructural windows that incorporate short-range (on the order of particle/feature size) as well as long-range (hundred times the particle/feature size) microstructural heterogeneities and spatial patterns at high resolution. The utility of the technique has been successfully demonstrated through its application to the 2D microstructures of the constituent particles in wrought Al-alloys, the 3D microstructure of discontinuously reinforced Al-alloy (DRA) composites containing SiC particles that have complex 3D shapes/morphologies and spatial clustering, and 3D microstructure of boron modified Ti-6Al-4V composites containing fine TiB whiskers and coarse primary TiB particles. The simulation parameters are correlated with the materials processing parameters (such as composition, particle size ratio, extrusion ratio, extrusion temperature, etc.), which enables the simulations of rational virtual 3D microstructures for the parametric studies on microstructure-properties relationships. The simulated microstructures have been implemented in the 3D finite-elements (FE)-based framework for simulations of micro-mechanical response and stress-strain curves. Finally, a new unbiased and assumption free dual-scale virtual cycloids probe for estimating surface area of 3D objects constructed by 2D serial section images is also presented.Ph.D.Committee Chair: Arun M. Gokhale; Committee Member: David Frost; Committee Member: Meilin Liu; Committee Member: Burton R Patterson; Committee Member: Min Zho

Final results of Borexino Phase-I on low energy solar neutrino spectroscopy

Borexino has been running since May 2007 at the LNGS with the primary goal of detecting solar neutrinos. The detector, a large, unsegmented liquid scintillator calorimeter characterized by unprecedented low levels of intrinsic radioactivity, is optimized for the study of the lower energy part of the spectrum. During the Phase-I (2007-2010) Borexino first detected and then precisely measured the flux of the 7Be solar neutrinos, ruled out any significant day-night asymmetry of their interaction rate, made the first direct observation of the pep neutrinos, and set the tightest upper limit on the flux of CNO neutrinos. In this paper we discuss the signal signature and provide a comprehensive description of the backgrounds, quantify their event rates, describe the methods for their identification, selection or subtraction, and describe data analysis. Key features are an extensive in situ calibration program using radioactive sources, the detailed modeling of the detector response, the ability to define an innermost fiducial volume with extremely low background via software cuts, and the excellent pulse-shape discrimination capability of the scintillator that allows particle identification. We report a measurement of the annual modulation of the 7 Be neutrino interaction rate. The period, the amplitude, and the phase of the observed modulation are consistent with the solar origin of these events, and the absence of their annual modulation is rejected with higher than 99% C.L. The physics implications of phase-I results in the context of the neutrino oscillation physics and solar models are presented

Cross-Layer Optimization of Network Performance over MIMO Wireless Mobile Channels

In the information theory, the channel capacity states the maximum amount of in­ formation which can be reliably transmitted over the communication channel. In the specific case of multiple-input multiple-output (MIMO) wireless systems, it is well recognized that the instantaneous capacity of MIMO systems is a random Gaussian process. Time variation of the capacity leads to the outages at instances when it falls below the transmission rate. The frequency of such events is known as outage probability. The cross-layer approach proposed in this work focuses on the effects of MIMO capacity outages on the network performance, providing a joint optimization of the MIMO communication system. For a constant rate transmission, the outage prob­ ability sensibly affects the amount of information correctly received at destination. Theoretically, the limit of the ergodic capacity in MIMO time-variant channels can be achieved by adapting the transmission rate to the capacity variation. With an accu­ rate channel state information, the capacity evolution can be predicted by a suitable autoregressive model based on the capacity time correlation. Taking into consider­ ation the joint effects of channel outage at the physical layer and buffer overflow at the medium access control (MAC) layer, the optimal transmission strategy is derived analytically through the Markov decision processes (MDP) theory. The adaptive pol­ icy obtained by MDP is optimal and maximizes the amount of information correctly received at the destination MAC layer (throughput of the system). Analytical results demonstrate the significant improvements of the optimal variable rate strategy com­ pared to a constant transmission rate strategy, in terms of both system throughput and probability of data los

Cross-Layer Optimization of Network Performance over MIMO Wireless Mobile Channels

In the information theory, the channel capacity states the maximum amount of information which can be reliably transmitted over the communication channel. In the specific case of multiple-input multiple-output (MIMO) wireless systems, it is well recognized that the instantaneous capacity of MIMO systems is a random Gaussian process. Time variation of the capacity leads to the outages at instances when it falls below the transmission rate. The frequency of such events is known as outage probability. The cross-layer approach proposed in this work focuses on the effects of MIMO capacity outages on the network performance, providing a joint optimization of the MIMO communication system. For a constant rate transmission, the outage probability sensibly affects the amount of information correctly received at destination. Theoretically, the limit of the ergodic capacity in MIMO time-variant channels can be achieved by adapting the transmission rate to the capacity variation. With an accurate channel state information, the capacity evolution can be predicted by a suitable autoregressive model based on the capacity time correlation. Taking into consideration the joint effects of channel outage at the physical layer and buffer overflow at the medium access control (MAC) layer, the optimal transmission strategy is derived analytically through the Markov decision processes (MDP) theory. The adaptive policy obtained by MDP is optimal and maximizes the amount of information correctly received at the destination MAC layer (throughput of the system). Analytical results demonstrate the significant improvements of the optimal variable rate strategy compared to a constant transmission rate strategy, in terms of both system throughput and probability of data loss

PlantGL : a Python-based geometric library for 3D plant modelling at different scales

In this paper, we present PlantGL, an open-source graphic toolkit for the creation, simulation and analysis of 3D virtual plants. This C++ geometric library is embedded in the Python language which makes it a powerful user-interactive platform for plant modelling in various biological application domains. PlantGL makes it possible to build and manipulate geometric models of plants or plant parts, ranging from tissues and organs to plant populations. Based on a scene graph augmented with primitives dedicated to plant representation, several methods are provided to create plant architectures from either field measurements or procedural algorithms. Because they reveal particularly useful in plant design and analysis, special attention has been paid to the definition and use of branching system envelopes. Several examples from different modelling applications illustrate how PlantGL can be used to construct, analyse or manipulate geometric models at different scales