2,104 research outputs found
Identification of Differential Agronomic Traits in Early Stage Teosinte, Flint, Dent, and Sugar (Sweet) Corn Varieties in Competition with Weeds
Weed competition reduces corn yield. Today’s corn monoculture relies heavily on herbicide inputs to maintain yield. However, teosinte, corn’s ancestor, was successfully grown in mixed production systems. Harnessing genes and traits that allow teosinte to be a better competitor but which may have been lost during corn domestication could help producers reduce herbicide inputs and maintain yield. The first step to finding those genes is to identify varieties of corn and lines of teosinte that have a higher tolerance of weeds or greater weed suppressive ability. Five introductions of teosinte and 14 varieties of corn (including dent, heritage, and sweet corn types) were cultivated with and without weed pressure. Early and end of season growth characteristics including leaf area, plant height, stem diameter, biomass, and yield, when possible, indicated a wide range of weed tolerance. Differences between weedy and weed-free treatments within a type ranged from 1-10% in corn height, 3-20% in leaf area, 1-27% in corn biomass (July), 0.4-28% in top collar height (September), and 2-17% in grain yield on a per cob basis at harvest (October). Varieties demonstrating the greatest and least deviations in these measurements between treatments have been selected for preliminary molecular analysis (data not available at this time). Identifying early season growth characteristics and gene expression associated with maintaining high yield under weed stress conditions can, in the long term, lead to better understanding the mechanisms of crop tolerance, its heritability, and reducing weed control inputs
Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach
In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance
Lines, Circles, Planes and Spheres
Let be a set of points in , no three collinear and not
all coplanar. If at most are coplanar and is sufficiently large, the
total number of planes determined is at least . For similar conditions and
sufficiently large , (inspired by the work of P. D. T. A. Elliott in
\cite{Ell67}) we also show that the number of spheres determined by points
is at least , and this bound is best
possible under its hypothesis. (By , we are denoting the
maximum number of three-point lines attainable by a configuration of
points, no four collinear, in the plane, i.e., the classic Orchard Problem.)
New lower bounds are also given for both lines and circles.Comment: 37 page
Plasmon-resonant gold nanorods as low backscattering albedo contrast agents for optical coherence tomography
Plasmon-resonant gold nanorods are demonstrated as low back-scattering albedo contrast agents for optical coherence tomography (OCT). We define the backscattering albedo, a′, as the ratio of the backscattering to extinction coefficient. Contrast agents which modify a' within the host tissue phantoms are detected with greater sensitivity by the differential OCT measurement of both a′ and extinction. Optimum sensitivity is achieved by maximizing the difference between contrast agents and tissue, |a′ca - a′tiss|. Low backscattering albedo gold nanorods (14 × 44 nm; λmax = 780 nm) within a high backscattering albedo tissue phantom with an uncertainty in concentration of 20% (randomized 2±0.4% intralipid) were readily detected at 82 ppm (by weight) in a regime where extinction alone could not discriminate nanorods. The estimated threshold of detection was 30 ppm
Imaging gold nanorods in excised human breast carcinoma by spectroscopic optical coherence tomography
Plasmon-resonant gold nanorods (GNRs) can serve as imaging agents for spectroscopic optical coherence tomography (SOCT). The aspect ratio of the GNRs is adjusted for maximum absorption in the far red to create a partial spectral overlap with the short-wavelength edge of the near-infrared SOCT imaging band. The spectroscopic absorption profile of the GNRs is incorporated into a depth-resolved algorithm for mapping the relative GNR density within OCT images. This technique enables us to image GNR distributions in excised human breast carcinomas, demonstrating their potential as OCT contrast agents in heterogeneous, highly scattering tissues
Combining Multiple Classifiers with Dynamic Weighted Voting
When a multiple classifier system is employed, one of the most popular methods to accomplish the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the efficiency of this type of voting generally results affected negatively. In this paper, new functions for dynamic weighting in classifier fusion are introduced. Experimental results demonstrate the advantages of these novel strategies over the simple voting scheme
Isovector soft dipole mode in 6Be
By using the 1H(6Li,6Be)n charge-exchange reaction, continuum states in 6Be
were populated up to E_t=16 MeV, E_t being the 6Be energy above its three-body
decay threshold. In kinematically complete measurements performed by detecting
alpha+p+p coincidences, an E_t spectrum of high statistics was obtained,
containing approximately ~5x10^6 events. The spectrum provides detailed
correlation information about the well-known 0^+ ground state of 6Be at
E_t=1.37 MeV and its 2^+ state at E_t=3.05 MeV. Moreover, a broad structure
extending from 4 to 16 MeV was observed. It contains negative parity states
populated by Delta L=1 angular momentum transfer without other significant
contributions. This structure can be interpreted as a novel phenomenon, i.e.
the isovector soft dipole mode associated with the 6Li ground state. The
population of this mode in the charge-exchange reaction is a dominant
phenomenon for this reaction, being responsible for about 60% of the cross
section obtained in the measured energy range.Comment: 8 pages, 7 figure
Phase transitions in a ferrofluid at magnetic field induced microphase separation
In the presence of a magnetic field applied perpendicular to a thin sample
layer, a suspension of magnetic colloidal particles (ferrofluid) can form
spatially modulated phases with a characteristic length determined by the
competition between dipolar forces and short-range forces opposing density
variations. We introduce models for thin-film ferrofluids in which
magnetization and particle density are viewed as independent variables and in
which the non-magnetic properties of the colloidal particles are described
either by a lattice-gas entropy or by the Carnahan-Starling free energy. Our
description is particularly well suited to the low-particle density regions
studied in many experiments. Within mean-field theory, we find isotropic,
hexagonal and stripe phases, separated in general by first-order phase
boundaries.Comment: 12 pages, RevTex, to appear in PR
Towards a formalism for mapping the spacetimes of massive compact objects: Bumpy black holes and their orbits
Observations have established that extremely compact, massive objects are
common in the universe. It is generally accepted that these objects are black
holes. As observations improve, it becomes possible to test this hypothesis in
ever greater detail. In particular, it is or will be possible to measure the
properties of orbits deep in the strong field of a black hole candidate (using
x-ray timing or with gravitational-waves) and to test whether they have the
characteristics of black hole orbits in general relativity. Such measurements
can be used to map the spacetime of a massive compact object, testing whether
the object's multipoles satisfy the strict constraints of the black hole
hypothesis. Such a test requires that we compare against objects with the
``wrong'' multipole structure. In this paper, we present tools for constructing
bumpy black holes: objects that are almost black holes, but that have some
multipoles with the wrong value. The spacetimes which we present are good deep
into the strong field of the object -- we do not use a large r expansion,
except to make contact with weak field intuition. Also, our spacetimes reduce
to the black hole spacetimes of general relativity when the ``bumpiness'' is
set to zero. We propose bumpy black holes as the foundation for a null
experiment: if black hole candidates are the black holes of general relativity,
their bumpiness should be zero. By comparing orbits in a bumpy spacetime with
those of an astrophysical source, observations should be able to test this
hypothesis, stringently testing whether they are the black holes of general
relativity. (Abridged)Comment: 16 pages + 2 appendices + 3 figures. Submitted to PR
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