In-Store Evaluation of Consumer Willingness to Pay for “Farm-Raised†Pre-Cooked Roast Beef: A Case Study

A choice-based conjoint experiment was used to examine consumer willingness to pay for a farm-raised pre-cooked roast beef product. Consumers were contacted in a grocery store and provided a sample of the pre-cooked product. Findings indicate there is a small, but statistically significant willingness-to-pay premium for the farm-raised product, suggesting that some product differentiation may result in higher prices for these products. The study outlines an approach to marketing research.beef, conjoint, convenience foods, experiments, in-store tests, surveys, Livestock Production/Industries, Marketing,

Shock-Strength Determination With Seeded and Seedless Laser Methods

Two nonintrusive laser diagnostics were independently used to demonstrate the measurement of time-averaged and spatially-resolved pressure change across a twodimensional (2-D) shock wave. The first method is Doppler global velocimetry (DGV) which uses water seeding and generates 2-D maps of 3-orthogonal components of velocity. A DGV-measured change in flow direction behind an oblique shock provides an indirect determination of pressure jump across the shock, when used with the known incoming Mach number and ideal shock relations (or Prandtl-Meyer flow equations for an expansion fan). This approach was demonstrated at Mach 2 on 2-D shocks and expansions generated from a flat plate at angles-of-attack approx. equals -2.4deg and +0.6deg, respectively. This technique also works for temperature jump (as well as pressure) and for normal shocks (as well as oblique). The second method, laser-induced thermal acoustics (LITA), is a seedless approach that was used to generate 1-D spatial profiles of streamwise Mach number, sound speed, pressure, and temperature across the same shock waves. Excellent agreement was obtained between the DGV and LITA methods, suggesting that either technique is viable for noninvasive shock-strength measurements

Bayesian Functional Principal Component Analysis using Relaxed Mutually Orthogonal Processes

Functional Principal Component Analysis (FPCA) is a prominent tool to characterize variability and reduce dimension of longitudinal and functional datasets. Bayesian implementations of FPCA are advantageous because of their ability to propagate uncertainty in subsequent modeling. To ease computation, many modeling approaches rely on the restrictive assumption that functional principal components can be represented through a pre-specified basis. Under this assumption, inference is sensitive to the basis, and misspecification can lead to erroneous results. Alternatively, we develop a flexible Bayesian FPCA model using Relaxed Mutually Orthogonal (ReMO) processes. We define ReMO processes to enforce mutual orthogonality between principal components to ensure identifiability of model parameters. The joint distribution of ReMO processes is governed by a penalty parameter that determines the degree to which the processes are mutually orthogonal and is related to ease of posterior computation. In comparison to other methods, FPCA using ReMO processes provides a more flexible, computationally convenient approach that facilitates accurate propagation of uncertainty. We demonstrate our proposed model using extensive simulation experiments and in an application to study the effects of breastfeeding status, illness, and demographic factors on weight dynamics in early childhood. Code is available on GitHub at https://github.com/jamesmatuk/ReMO-FPC

Pelvic measurements and calving difficulty (1997)

Although researchers agree that birth weight is the most important measurable trait affecting calving difficulty, there is evidence that the size and shape of the pelvis also affect a heifer's ability to calve.New February 1997 -- Extension web site

Artificial Neural Network Performance Model for Parallel Particle Transport Calculation

There is a need to improve the predictive capability of high-fidelity simulations of physical phenomena that include the transport of thermal radiation and/or other subatomic particles. There are many ingredients of improved capability, including solution algorithms that more efficiently use modern massively parallel computers. The most time-consuming element of many widely used particle-transport methods is the transport sweep, in which the particle intensity—a function of position, energy, and direction—is calculated given the most recent estimate for the collisional source. The intensity in a given spatial cell depends on the intensity entering from neighboring cells in the given direction, which imposes restrictions on the order of calculations and implies that cells must communicate exiting intensities to their downstream neighbors. Such dependen- cies and communication requirements make parallel execution more difficult. A parallel transport calculation in Texas A&M’s state-of-the-art PDT code partitions the spatial domain across pro- cessors as directed by partitioning parameters. It aggregates spatial cells into cellsets, directions into anglesets, and energy groups into groupsets, as directed by aggregation parameters. A single work unit during a sweep calculates particle intensities in a single cellset/angleset/groupset combi- nation. At each “stage” of the sweep every processor with available work executes one work unit and communicates outflow intensities to processors responsible for adjacent downstream cellsets. The ingredients of the “optimal sweep” methodology developed by Texas A&M in collaboration with the NNSA labs are: (i) a provably optimal scheduling algorithm, which executes the sweep in the minimum possible number of stages for any given partitioning and aggregation factors; (ii) a performance model that predicts sweep time for that execution; and (iii) an algorithm that chooses partitioning and aggregation factors that minimize sweep time. Here we explore the use of Arti- ficial Neural Networks (ANNs) for such a model, and its memory-use counterpart, and compare against our previous models. We design simple networks that have the ability to replicate pre- vious models but also to augment those models with nonlinear corrections if this better fits the data. These simple nonlinear ANNs outperform our previous models, reducing average prediction ii errors from ≈ 41% to ≈ 21% for some problems of interest, although large maximum errors are observed for both models. Additionally PDT reports unexpected results for parallel problems, pos- sibly contribution to the large maximum observed errors. Despite this observation, both the ANN based nonlinear model and our previous model show signs of fruitful practical use for an algorithm such as the one described in (iii). The memory-usage model shows promising results predicting memory usage within ≈ 0.024 GB for out of sample data points

An exact study of charge-spin separation, pairing fluctuations and pseudogaps in four-site Hubbard clusters

An exact study of charge-spin separation, pairing fluctuations and pseudogaps is carried out by combining the analytical eigenvalues of the four-site Hubbard clusters with the grand canonical and canonical ensemble approaches in a multidimensional parameter space of temperature (T), magnetic field (h), on-site interaction (U) and chemical potential. Our results, near the average number of electrons =3, strongly suggest the existence of a critical parameter U_{c}(T) for the localization of electrons and a particle-hole binding (positive) gap at U>U_{c}(T), with a zero temperature quantum critical point, U_{c}(0)=4.584. For U<U_{c}(T), particle-particle pair binding is found with a (positive) pairing gap. The ground state degeneracy is lifted at U>U_c(T) and the cluster becomes a Mott-Hubbard like insulator due to the presence of energy gaps at all (allowed) integer numbers of electrons. In contrast, for U< U_c(T), we find an electron pair binding instability at finite temperature near =3, which manifests a possible pairing mechanism, a precursor to superconductivity in small clusters. In addition, the resulting phase diagram consisting of charge and spin pseudogaps, antiferromagnetic correlations, hole pairing with competing hole-rich (=2), hole-poor (=4) and magnetic (=3) regions in the ensemble of clusters near 1/8 filling closely resembles the phase diagrams and inhomogeneous phase separation recently found in the family of doped high T_c cuprates.Comment: 10 pages, 7 figure

Using a virtual reality cricket simulator to explore the effects of pressure, competition anxiety on batting performance in cricket

Virtual reality (VR) has created opportunities to innovatively re-imagine the way we examine the relations between pressure, competition anxiety and performance. This study aimed to determine the efficacy of VR as a means of measuring the effects of competition anxiety when pressure manipulations are applied while participants bat in a cricket batting VR simulator. The twenty-eight male participants who took part in two experiments were divided into a high (14, mean age: 22.94, SD: 5.4) and a low skill group (14; mean age: 23.55, SD: 9.9). The aim of the first experiment was to validate the VR simulator as a tool that could capture differences in batting performance between a high and low skilled group. The results showed that high skill participants not only scored significantly higher run rates than low skill participants, but they outperformed the low skill group in all performance measures including higher incidences of correct foot placements that reflect better anticipatory responses. Having established the VR batting simulator as being a reliable tool for capturing batting dynamics, experiment 2 aimed to explore the effects of a pressure manipulation on competition anxiety and batting performance. All measures of competition anxiety were significantly greater for both groups in the high-pressure condition compared to the two low-pressure conditions (p &lt; 0.001). The magnitude of this effect was greater in the low skill group for cognitive (0.59) and somatic (0.794) anxiety. Despite anxiety levels significantly increasing in the high-pressure condition, no significant negative changes to batting performance were found for either group, with both groups actually demonstrating performance improvements. Overall, the findings show how a cricket batting virtual reality simulator can be used as a tool to measure the effects of pressure on competition anxiety and batting performance in tasks involving dynamic skill execution.</p