Dynamics of a Classical Particle in a Quasi Periodic Potential

We study the dynamics of a one-dimensional classical particle in a space and time dependent potential with randomly chosen parameters. The focus of this work is a quasi-periodic potential, which only includes a finite number of Fourier components. The momentum is calculated analytically for short time within a self-consistent approximation, under certain conditions. We find that the dynamics can be described by a model of a random walk between the Chirikov resonances, which are resonances between the particle momentum and the Fourier components of the potential. We use numerical methods to test these results and to evaluate the important properties, such as the characteristic hopping time between the resonances. This work sheds light on the short time dynamics induced by potentials which are relevant for optics and atom optics

GENERALIZED LINEAR MIXED MODELS: AN APPLICATION

The purpose of this paper is to present a specific application of the generalized linear mixed model. Often of interest to animal-breeders is the estimation of genetic parameters associated with certain traits. When the trait is measured in terms of a normally distributed response variable, standard variance-component estimation and mixed-model procedures can be used. Increasingly, breeders are interested in categorical traits (degree of calving difficulty, number born, etc.). An application of the generalized linear mixed to an animal breeding study of the number of lambs born alive will be presented. We will show how the model is determined, how the estimation equations are formed, and the resulting inference

ANALYSIS OF THE SPATIAL DISTRIBUTION OF SUGARBEET PLANTS

The spatial distribution of emerged sugarbeet plants is an important aspect of the performance of sugarbeet planters. Three major components influencing the spatial distribution are the ability to drop a single seed at a time, the ability to drop the seeds a fixed distance apart, and the ability of the seed to emerge. A model has been developed to describe the distribution of the spacing between emerged sugarbeet plants. The model consists of a mixture of normal and gamma distributions. The spatial data consists of the distance between neighboring emerged plants. Spatial data was collected on 7 planters operated at 3 speeds using both pelleted and encrusted seeds. Four replicates were obtained of each treatment combination. Approximate maximum likelihood estimates of the parameters were obtained separately for each replicate of the treatment combinations

GENERALIZED LINEAR MIXED MODELS - AN OVERVIEW

Generalized linear models provide a methodology for doing regression and ANOV A-type analysis with data whose errors are not necessarily normally-distributed. Common applications in agriculture include categorical data, survival analysis, bioassay, etc. Most of the literature and most of the available computing software for generalized linear models applies to cases in which all model effects are fixed. However, many agricultural research applications lead to mixed or random effects models: split-plot experiments, animal- and plant-breeding studies, multi-location studies, etc. Recently, through a variety of efforts in a number of contexts, a general framework for generalized linear models with random effects, the generalized linear mixed model, has been developed . The purpose of this presentation is to present an overview of the methodology for generalized mixed linear models. Relevant background, estimating equations, and general approaches to interval estimation and hypothesis testing will be presented. Methods will be illustrated via a small data set involving binary data

A BAYESIAN GWAS METHOD UTILIZING HAPLOTYPE CLUSTERS FOR A COMPOSITE BREED POPULATION

Commercial beef cattle are often composites of multiple breeds. Current methods used to produce genomic predictors are based on the underlying assumption of animals being sampled from a homogeneous population. As a result, the predictors can perform poorly when used to predict the relative genetic merit of animals whose breed composition are different. In part, this is due to the changes in linkage disequilibrium between the markers and the quantitative trait loci as we move from one breed to the next. An alternative model based on breed specific haplotype clusters was developed to allow for differences in linkage disequilibrium across multiple breeds. The haplotype clusters were modeled as hidden states in a hidden Markov model where the genomic effects are associated with loci located on the unobserved clusters. Similar to the Bayes C model, we can model the genomic effects at the loci using a prior, which consists of a mixture of a multivariate normal and a point mass at zero distribution. The model will be used to construct genomic predictors using records on 6,552 cattle genotyped for 99,827 mapped SNPs representing various fractions of three different breeds

A BAYESIAN GWAS METHOD UTILIZING HAPLOTYPE CLUSTERS FOR A COMPOSITE BREED POPULATION

Commercial beef cattle are often composites of multiple breeds. Current methods used to produce genomic predictors are based on the underlying assumption of animals being sampled from a homogeneous population. As a result, the predictors can perform poorly when used to predict the relative genetic merit of animals whose breed composition are different. In part, this is due to the changes in linkage disequilibrium between the markers and the quantitative trait loci as we move from one breed to the next. An alternative model based on breed specific haplotype clusters was developed to allow for differences in linkage disequilibrium across multiple breeds. The haplotype clusters were modeled as hidden states in a hidden Markov model where the genomic effects are associated with loci located on the unobserved clusters. Similar to the Bayes C model, we can model the genomic effects at the loci using a prior, which consists of a mixture of a multivariate normal and a point mass at zero distribution. The model will be used to construct genomic predictors using records on 5,000 cattle genotyped for 99,827 mapped SNPs representing various fractions of three different breeds

Self-Organized Resonance during Search of a Diverse Chemical Space

Recent studies of active matter have stimulated interest in the driven self-assembly of complex structures. Phenomenological modeling of particular examples has yielded insight, but general thermodynamic principles unifying the rich diversity of behaviors observed have been elusive. Here, we study the stochastic search of a toy chemical space by a collection of reacting Brownian particles subject to periodic forcing. We observe the emergence of an adaptive resonance in the system matched to the drive frequency, and show that the increased work absorption by these resonant structures is key to their stabilization. Our findings are consistent with a recently proposed thermodynamic mechanism for far-from-equilibrium self-organization