2,759 research outputs found
PASTEURELLOSIS IN DUCK IN WEST BENGAL
Two hundred sixty four samples were collected from heart blood, liver, spleen and femur of 85 khaki Campbell ducks of which P. multocida could be isolated from 4 (4.70%) birds. Out of 4 samples, organisms could be isolated from heart blood of one ducklings liver and femur of one duck each. All the isolates were found positive to catalase, oxidase, indole, nitrate reduction test and negative to methyl red, Voges-Proskaur, citrate utilization,
H2S production and triple sugar iron test. The isolates fermented glucose and manitol without production of gas and non-fermented lactose, salicin, dulcitol and inositol. The isolates were non-motile and pathogenic to mice. All isolates of P. multocida were sensitive to amoxiclav, chloramphenicol, gentamicin and three isolates were sensitive to co-trimoxazole. All were moderately sensitive to amikacin, cefotaxime, neomycin and norfloxacin and resistant
to ciprofloxacin and lomefloxacin
Temperature dependent optical studies of TiCoO
We present the results of Raman and photoluminescence (PL) studies on
epitaxial anatase phase TiCoO films for = 0-0.07, grown by
pulsed laser deposition. The low doped system (=0.01 and 0.02) shows a Curie
temperature of ~700 K in the as-grown state. The Raman spectra from the doped
and undoped films confirm their anatase phase. The photoluminescence spectrum
is characterized by a broad emission from self-trapped excitons (STE) at 2.3 eV
at temperatures below 120 K. This peak is characteristic of the anatase-phase
TiO and shows a small blueshift with increasing doping concentration. In
addition to the emission from STE, the Co-doped samples show two emission lines
at 2.77 eV and 2.94 eV that are absent in the undoped film indicative of a
spin-flip energy.Comment: 8 pages, 4 figure
Volume preserving multidimensional integrable systems and Nambu--Poisson geometry
In this paper we study generalized classes of volume preserving
multidimensional integrable systems via Nambu--Poisson mechanics. These
integrable systems belong to the same class of dispersionless KP type equation.
Hence they bear a close resemblance to the self dual Einstein equation. All
these dispersionless KP and dToda type equations can be studied via twistor
geometry, by using the method of Gindikin's pencil of two forms. Following this
approach we study the twistor construction of our volume preserving systems
A note on multi-dimensional Camassa-Holm type systems on the torus
We present a -component nonlinear evolutionary PDE which includes the
-dimensional versions of the Camassa-Holm and the Hunter-Saxton systems as
well as their partially averaged variations. Our goal is to apply Arnold's
[V.I. Arnold, Sur la g\'eom\'etrie diff\'erentielle des groupes de Lie de
dimension infinie et ses applications \`a l'hydrodynamique des fluides
parfaits. Ann. Inst. Fourier (Grenoble) 16 (1966) 319-361], [D.G. Ebin and J.E.
Marsden, Groups of diffeomorphisms and the motion of an incompressible fluid.
Ann. of Math. 92(2) (1970) 102-163] geometric formalism to this general
equation in order to obtain results on well-posedness, conservation laws or
stability of its solutions. Following the line of arguments of the paper [M.
Kohlmann, The two-dimensional periodic -equation on the diffeomorphism group
of the torus. J. Phys. A.: Math. Theor. 44 (2011) 465205 (17 pp.)] we present
geometric aspects of a two-dimensional periodic --equation on the
diffeomorphism group of the torus in this context.Comment: 14 page
FlowerPhenoNet: Automated Flower Detection from Multi-View Image Sequences Using Deep Neural Networks for Temporal Plant Phenotyping Analysis
A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as temporal phenotypes and indicative of the plant’s growth pattern and vigor. In this paper, we introduce a novel method called FlowerPhenoNet, which uses deep neural networks for detecting flowers from multiview image sequences for high-throughput temporal plant phenotyping analysis. Following flower detection, a set of novel flower-based phenotypes are computed, e.g., the day of emergence of the first flower in a plant’s life cycle, the total number of flowers present in the plant at a given time, the highest number of flowers bloomed in the plant, growth trajectory of a flower, and the blooming trajectory of a plant. To develop a new algorithm and facilitate performance evaluation based on experimental analysis, a benchmark dataset is indispensable. Thus, we introduce a benchmark dataset called FlowerPheno, which comprises image sequences of three flowering plant species, e.g., sunflower, coleus, and canna, captured by a visible light camera in a high-throughput plant phenotyping platform from multiple view angles. The experimental analyses on the FlowerPheno dataset demonstrate the efficacy of the FlowerPhenoNet
Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks
Exact calculation of electronic properties of molecules is a fundamental step
for intelligent and rational compounds and materials design. The intrinsically
graph-like and non-vectorial nature of molecular data generates a unique and
challenging machine learning problem. In this paper we embrace a learning from
scratch approach where the quantum mechanical electronic properties of
molecules are predicted directly from the raw molecular geometry, similar to
some recent works. But, unlike these previous endeavors, our study suggests a
benefit from combining molecular geometry embedded in the Coulomb matrix with
the atomic composition of molecules. Using the new combined features in a
Bayesian regularized neural networks, our results improve well-known results
from the literature on the QM7 dataset from a mean absolute error of 3.51
kcal/mol down to 3.0 kcal/mol.Comment: Under review ICANN 201
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A global atmospheric electricity monitoring network for climate and geophysical research
The Global atmospheric Electric Circuit (GEC) is a fundamental coupling network of the climate system connecting electrically disturbed weather regions with fair weather regions across the planet. The GEC sustains the fair weather electric field (or potential gradient, PG) which is present globally and can be measured routinely at the surface using durable instrumentation such as modern electric field mills, which are now widely deployed internationally. In contrast to lightning or magnetic fields, fair weather PG cannot be measured remotely. Despite the existence of many PG datasets (both contemporary and historical), few attempts have been made to coordinate and integrate these fragmented surface measurements within a global framework. Such a synthesis is important elvinin order to fully study major influences on the GEC such as climate variations and space weather effects, as well as more local atmospheric electrical processes such as cloud electrification, lightning initiation, and dust and aerosol charging.
The GloCAEM (Global Coordination of Atmospheric Electricity Measurements) project has brought together experts in atmospheric electricity to make the first steps towards an effective global network for atmospheric electricity monitoring, which will provide data in near real time. Data from all sites are available in identically-formatted files, at both one second and one minute temporal resolution, along with meteorological data (wherever available) for ease of interpretation of electrical measurements. This work describes the details of the GloCAEM database and presents what is likely to be the largest single analysis of PG data performed from multiple datasets at geographically distinct locations. Analysis of the diurnal variation in PG from all 17 GloCAEM sites demonstrates that the majority of sites show two daily maxima, characteristic of local influences on the PG, such as the sunrise effect. Data analysis methods to minimise such effects are presented and recommendations provided on the most suitable GloCAEM sites for the study of various scientific phenomena. The use of the dataset for a further understanding of the GEC is also demonstrated, in particular for more detailed characterization of day-to-day global circuit variability. Such coordinated effort enables deeper insight into PG phenomenology which goes beyond single-location PG measurements, providing a simple measurement of global thunderstorm variability on a day-to-day timescale. The creation of the GloCAEM database is likely to enable much more effective study of atmospheric electricity variables than has ever been possible before, which will improve our understanding of the role of atmospheric electricity in the complex processes underlying weather and climate
A new method of synthesis of bicyclic compounds
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