CONSUMER RESPONSE TO GENETICALLY MODIFIED FOODS: MARKET SEGMENT ANALYSIS AND IMPLICATIONS FOR PRODUCERS AND POLICY MAKERS

Conjoint analysis is used to elicit consumer preferences for attributes of genetically modified foods. Market segments are identified based on a cluster analysis of respondents' preferences for brand, price, and GMO content. A logit analysis is used to analyze consumer characteristics associated with the acceptance of GMO foods. Those consumers who were most risk averse, most likely to believe that GMOs improved the quality or safety of food, and most knowledgeable about biotechnology were the most likely to be accepting of GMO foods. These findings are used to develop implications for producers and regulators of GMO foods.Agricultural and Food Policy, Consumer/Household Economics,

Structures of K0.05Na0.95NbO3 (50–300 K) and K0.30Na0.70NbO3 (100–200 K)

Rietveld refinement using neutron powder diffraction data is reported for the potential lead-free piezoelectric material KxNa1 - xNbO3 (x = 0.05, x = 0.3) at low temperatures. The structures were determined to be of rhombohedral symmetry, space group R3c, with the tilt system a-a-a- for both compositions. It was found that some of the structural parameters differ significantly in the two structures, and particularly the NbO6 octahedral strains as a function of temperature. The 300 K profile for K0.05Na0.95NbO3 shows the coexistence of rhombohedral and monoclinic phases, which indicates that the phase boundary is close to room temperature; the phase boundary for K0.30Na0.70NbO3 is found to be at approximately 180 K

Atomic radius and charge parameter uncertainty in biomolecular solvation energy calculations

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is under-determined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a new method for quantifying this uncertainty in implicit solvation calculations of small molecules using surrogate models based on generalized polynomial chaos (gPC) expansions. There are relatively few atom types used to specify radii parameters in implicit solvation calculations; therefore, surrogate models for these low-dimensional spaces could be constructed using least-squares fitting. However, there are many more types of atomic charges; therefore, construction of surrogate models for the charge parameter space requires compressed sensing combined with an iterative rotation method to enhance problem sparsity. We demonstrate the application of the method by presenting results for the uncertainties in small molecule solvation energies based on these approaches. The method presented in this paper is a promising approach for efficiently quantifying uncertainty in a wide range of force field parameterization problems, including those beyond continuum solvation calculations.The intent of this study is to provide a way for developers of implicit solvent model parameter sets to understand the sensitivity of their target properties (solvation energy) on underlying choices for solute radius and charge parameters

Phase and micromotion of Bose-Einstein condensates in a time-averaged ring trap

Rapidly scanning magnetic and optical dipole traps have been widely utilised to form time-averaged potentials for ultracold quantum gas experiments. Here we theoretically and experimentally characterise the dynamic properties of Bose-Einstein condensates in ring-shaped potentials that are formed by scanning an optical dipole beam in a circular trajectory. We find that unidirectional scanning leads to a non-trivial phase profile of the condensate that can be approximated analytically using the concept of phase imprinting. While the phase profile is not accessible through in-trap imaging, time-of-flight expansion manifests clear density signatures of an in-trap phase step in the condensate, coincident with the instantaneous position of the scanning beam. The phase step remains significant even when scanning the beam at frequencies two orders of magnitude larger than the characteristic frequency of the trap. We map out the phase and density properties of the condensate in the scanning trap, both experimentally and using numerical simulations, and find excellent agreement. Furthermore, we demonstrate that bidirectional scanning eliminated the phase gradient, rendering the system more suitable for coherent matter wave interferometry.Comment: 10 pages, 7 figure

Fast microwave-assisted synthesis of Li-stuffed garnets and insights into Li diffusion from muon spin spectroscopy

Lithium-stuffed garnets attract huge attention due to their outstanding potential as solid-state electrolytes for lithium batteries. However, there exists a persistent challenge in the reliable synthesis of these complex functional oxides together with a lack of complete understanding of the lithium-ion diffusion mechanisms in these important materials. Addressing these issues is critical to realizing the application of garnet materials as electrolytes in all solid-state lithium-ion batteries. In this work, a cubic phase garnet of nominal composition Li6.5Al0.25La2.92Zr2O12 is synthesized through a microwave-assisted solid-state route for the first time, reducing considerably the reaction times and heating temperatures. Lithium-ion diffusion behavior is investigated by electrochemical impedance spectroscopy (EIS) and state-of-art muon spin relaxation (μSR) spectroscopy, displaying activation energies of 0.55 ± 0.03 eV and 0.19 ± 0.01 eV respectively. This difference arises from the high inter-grain resistance, which contributes to the total resistance in EIS measurements. In contrast, μSR acts as a local probe providing insights on the order of the lattice, giving an estimated value of 4.62 × 10−11 cm2 s−1 for the lithium diffusion coefficient. These results demonstrate the potential of this lithium-stuffed garnet as a solid-state electrolyte for all-solid state lithium-ion batteries, an area of growing interest in the energy storage community

From \u3cem\u3eBoard of Regents\u3c/em\u3e to \u3cem\u3eO\u27Bannon\u3c/em\u3e: How Antitrust and Media Rights Have Influenced College Football

Non

Variable-free exploration of stochastic models: a gene regulatory network example

Finding coarse-grained, low-dimensional descriptions is an important task in the analysis of complex, stochastic models of gene regulatory networks. This task involves (a) identifying observables that best describe the state of these complex systems and (b) characterizing the dynamics of the observables. In a previous paper [13], we assumed that good observables were known a priori, and presented an equation-free approach to approximate coarse-grained quantities (i.e, effective drift and diffusion coefficients) that characterize the long-time behavior of the observables. Here we use diffusion maps [9] to extract appropriate observables ("reduction coordinates") in an automated fashion; these involve the leading eigenvectors of a weighted Laplacian on a graph constructed from network simulation data. We present lifting and restriction procedures for translating between physical variables and these data-based observables. These procedures allow us to perform equation-free coarse-grained, computations characterizing the long-term dynamics through the design and processing of short bursts of stochastic simulation initialized at appropriate values of the data-based observables.Comment: 26 pages, 9 figure