A Bayesian-Influence Model for Error Probability Analysis of Combine Operations in Harvesting

Harvesting is one of the most important agricultural operations because it captures the value from the entire cropping season. In modern agriculture, grain harvesting has been mechanized through the combine harvester. A combine harvester enables highly productive crop harvesting. Combine harvesting performance depends on the highly variable skill of combine operators and associated operator error. An approach was developed to analyze the risk of the combine harvesting operation as it relates to operator error. Specifically, a risk analysis model was built based on a task analysis from operator interviews and estimates of the probability of operator error. This paper employs a Bayesian approach to assess risks in combine operation. This approach applies a Bayesian Belief Network to agriculture operations, which represents a new application for this risk analysis tool. Sensitivity analysis of different errors and operator skill levels was also performed. The preliminary results indicate that a reduction of human operator action errors can substantially improve the outcomes of the human-machine interaction

Cluster Algorithms for Quantum Impurity Models and Mesoscopic Kondo Physics

Nanoscale physics and dynamical mean field theory have both generated increased interest in complex quantum impurity problems and so have focused attention on the need for flexible quantum impurity solvers. Here we demonstrate that the mapping of single quantum impurity problems onto spin-chains can be exploited to yield a powerful and extremely flexible impurity solver. We implement this cluster algorithm explicitly for the Anderson and Kondo Hamiltonians, and illustrate its use in the ``mesoscopic Kondo problem''. To study universal Kondo physics, a large ratio between the effective bandwidth $D_\mathrm{eff}$ and the temperature $T$ is required; our cluster algorithm treats the mesoscopic fluctuations exactly while being able to approach the large $D_\mathrm{eff}/T$ limit with ease. We emphasize that the flexibility of our method allows it to tackle a wide variety of quantum impurity problems; thus, it may also be relevant to the dynamical mean field theory of lattice problems.Comment: 4 pages, 3 figure

Superconductivity and antiferromagnetism in a hard-core boson spin-1 model in two dimensions

A model of hard-core bosons and spin-1 sites with single-ion anisotropy is proposed to approximately describe hole pairs moving in a background of singlets and triplets with the aim of exploring the relationship between superconductivity and antiferromagnetism. The properties of this model at zero temperature were investigated using quantum Monte Carlo techniques. The most important feature found is the suppression of superconductivity, as long range coherence of preformed pairs, due to the presence of both antiferromagnetism and $S^z=\pm 1$ excitations. Indications of charge ordered and other phases are also discussed.Comment: One figure, one reference, adde

Universal scaling at field-induced magnetic phase transitions

We study field-induced magnetic order in cubic lattices of dimers with antiferromagnetic Heisenberg interactions. The thermal critical exponents at the quantum phase transition from a spin liquid to a magnetically ordered phase are determined from Stochastic Series Expansion Quantum Monte Carlo simulations. These exponents are independent of the interdimer coupling ratios, and converge to the value obtained by considering the transition as a Bose-Einstein condensation of magnons, alpha_(BEC) = 1.5. The scaling results are of direct relevance to the spin-dimer systems TlCuCl_3 and KCuCl_3, and explain the broad range of exponents reported for field-induced ordering transitions.Comment: 4 pages, 4 eps-figure