Derived Demand for Cattle Feeding Inputs

Derived demand relationships among four weight categories of feeder cattle entering Texas feedlots and feed were examined using a Generalized McFadden dual cost function specified as an error correction model. Relationships among own- and cross-price elasticities provide evidence for at least two cattle feeding enterprises, feeding lightweight feeder cattle (calves) and feeding heavier cattle. These results indicate systematic differences in demand relationships among the different weight classes, providing explanation and insight into mixed results from earlier studies. Seasonality differed across weight categories, providing additional support for multiple cattle feeding enterprises. A third step was added to the Engle-Granger two-step estimation procedure to incorporate information provided in the second step.Research Methods/ Statistical Methods,

Derived Demand for Cattle Feeding Inputs

Derived demand relationships among four weight categories of feeder cattle entering Texas feedlots and their feed consumed are examined using a generalized McFadden dual cost function. Results demonstrate systematic differences in demand relationships among different weight categories. Positive cross-price elasticities among the three heaviest weight categories are consistent with input substitution among weight categories and consistent with objective functions associated with optimal placement weight. Anomalies in the form of negative cross-price elasticities between weight categories provide evidence for an alternative objective function associated with longer term feeding of light-weight feeder cattle. Results also demonstrate seasonality differences across weight categories.cattle feeding, derived demand, elasticity, feeder cattle, generalized McFadden cost function, Agribusiness, Demand and Price Analysis, Livestock Production/Industries,

CONTROLLING WILDLIFE AND LIVESTOCK DISEASE WITH ENDOGENOUS ON-FARM BIOSECURITY

The spread of infectious disease among and between wild and domesticated animals has become a major problem worldwide. We analyze the socially optimal management of wildlife and livestock, including choices involving environmental habitat variables and on-farm biosecurity controls, when wildlife and livestock can spread an infectious disease to each other. The model is applied to the problem of bovine tuberculosis among Michigan white-tailed deer. The optimum is a cycle in which the disease remains endemic in the wildlife, but in which the cattle herd is depleted when the prevalence rate in deer grows too large.Research Methods/ Statistical Methods,

Fully quantum mechanical dynamic analysis of single-photon transport in a single-mode waveguide coupled to a traveling-wave resonator

We analyze the dynamics of single photon transport in a single-mode waveguide coupled to a micro-optical resonator using a fully quantum mechanical model. We examine the propagation of a single-photon Gaussian packet through the system under various coupling conditions. We review the theory of single photon transport phenomena as applied to the system and we develop a discussion on the numerical technique we used to solve for dynamical behavior of the quantized field. To demonstrate our method and to establish robust single photon results, we study the process of adiabatically lowering or raising the energy of a single photon trapped in an optical resonator under active tuning of the resonator. We show that our fully quantum mechanical approach reproduces the semi-classical result in the appropriate limit and that the adiabatic invariant has the same form in each case. Finally, we explore the trapping of a single photon in a system of dynamically tuned, coupled optical cavities.Comment: 24 pages, 10 figure

Rigorous Calculations of Non-Abelian Statistics in the Kitaev Honeycomb Model

We develop a rigorous and highly accurate technique for calculation of the Berry phase in systems with a quadratic Hamiltonian within the context of the Kitaev honeycomb lattice model. The method is based on the recently found solution of the model which uses the Jordan-Wigner-type fermionization in an exact effective spin-hardcore boson representation. We specifically simulate the braiding of two non-Abelian vortices (anyons) in a four vortex system characterized by a two-fold degenerate ground state. The result of the braiding is the non-Abelian Berry matrix which is in excellent agreement with the predictions of the effective field theory. The most precise results of our simulation are characterized by an error on the order of $10^{-5}$ or lower. We observe exponential decay of the error with the distance between vortices, studied in the range from one to nine plaquettes. We also study its correlation with the involved energy gaps and provide preliminary analysis of the relevant adiabaticity conditions. The work allows to investigate the Berry phase in other lattice models including the Yao-Kivelson model and particularly the square-octagon model. It also opens the possibility of studying the Berry phase under non-adiabatic and other effects which may constitute important sources of errors in topological quantum computation.Comment: 27 pages, 9 figures, 3 appendice

Si photocathode with Ag-supported dendritic Cu catalyst for CO2 reduction

Si photocathodes integrated with Ag-supported dendritic Cu catalysts are used to perform light-driven reduction of CO2 to C2 and C3 products in aqueous solution. A back illumination geometry with an n-type Si absorber was used to permit the use of absorbing metallic catalysts. Selective carrier collection was accomplished by a p+ implantation on the illumination side and an n+ implantation followed by atomic layer deposition of TiO2 on the electrolyte site. The Ag-supported dendritic Cu CO2 reduction catalyst was formed by evaporation of Ag followed by high-rate electrodeposition of Cu to form a high surface area structure. Under simulated 1 sun illumination in 0.1 M CsHCO3 saturated with CO2, the photovoltage generated by the Si (∼600 mV) enables C2 and C3 products to be produced at -0.4 vs. RHE. Texturing of both sides of the Si increases the light-limited current density, due to reduced reflection on the illumination side, and also deceases the onset potential. Under simulated diurnal illumination conditions photocathodes maintain over 60% faradaic efficiency to hydrocarbon and oxygenate products (mainly ethylene, ethanol, propanol) for several days. After 10 days of testing, contamination from the counter electrode is observed, which causes an increase in hydrogen production. This effect is mitigated by a regeneration procedure which restores the original catalyst selectivity. A tandem, self-powered CO2 reduction device was formed by coupling a Si photocathode with two series-connected semitransparent CH3NH3PbI3 perovskite solar cells, achieving an efficiency for the conversion of sunlight to hydrocarbons and oxygenates of 1.5% (3.5% for all products)

Finite temperature effects on cosmological baryon diffusion and inhomogeneous Big-Bang nucleosynthesis

We have studied finite temperature corrections to the baryon transport cross sections and diffusion coefficients. These corrections are based upon the recently computed renormalized electron mass and the modified state density due to the background thermal bath in the early universe. It is found that the optimum nucleosynthesis yields computed using our diffusion coefficients shift to longer distance scales by a factor of about 3. We also find that the minimum value of $^4 He$ abundance decreases by $\Delta Y_p \simeq 0.01$ while $D$ and $^7 Li$ increase. Effects of these results on constraints from primordial nucleosynthesis are discussed. In particular, we find that a large baryonic contribution to the closure density (\Omega_b h_{50}^{2} \lsim 0.4) may be allowed in inhomogeneous models corrected for finite temperature.Comment: 7 pages, 6 figures, submitted to Phys. Rev.

NcPred for accurate nuclear protein prediction using n-mer statistics with various classification algorithms

Prediction of nuclear proteins is one of the major challenges in genome annotation. A method, NcPred is described, for predicting nuclear proteins with higher accuracy exploiting n-mer statistics with different classification algorithms namely Alternating Decision (AD) Tree, Best First (BF) Tree, Random Tree and Adaptive (Ada) Boost. On BaCello dataset [1], NcPred improves about 20% accuracy with Random Tree and about 10% sensitivity with Ada Boost for Animal proteins compared to existing techniques. It also increases the accuracy of Fungal protein prediction by 20% and recall by 4% with AD Tree. In case of Human protein, the accuracy is improved by about 25% and sensitivity about 10% with BF Tree. Performance analysis of NcPred clearly demonstrates its suitability over the contemporary in-silico nuclear protein classification research

Optoelectronic Reservoir Computing

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.Comment: Contains main paper and two Supplementary Material