Chickpea Abiotic Stresses: Combating Drought, Heat and Cold

Chickpea is an important legume providing dietary proteins to both humans and animals. It also ameliorates soil nitrogen through biological nitrogen fixation. Drought, heat and cold are important factors among abiotic stresses limiting production in chickpea. Identification, validation and integration of agronomic, physiological and biochemical traits into breeding programs could lead to increased rates of genetic gain and the development of better adapted cultivars to abiotic stress conditions. This chapter illustrates the effects of stresses on chickpea growth and development. It also reviews the various traits and their relationship with grain yield under stress and proposes recommendation for future breeding

Structural and electrical properties of Nb-substituted LiTa1-xNbxO3

Single phase LiTa1-xNbxO3 solid solution with 0.00 ≤ x ≤ 1.00 was successfully synthesised via conventional solid-state method at 950°C for 24 h. These materials were refined and fully indexed with hexagonal crystal system, space group of R3c; lattice parameters, a ranging from 5.1410(6) Ǻ to 5.1471(3) Ǻ and c ranging from 13.7467(1) Ǻ to 13.8341(1) Ǻ; with α = β = 90° and γ = 120°. Variation of the lattice parameters in these materials was found to be negligibly small throughout the subsolidus solution. No thermal event was detected within the studied temperature range of 50 to 1000°C. The electrical properties of samples were characterised by AC impedance analyser, HP4192A at temperature ranging from room temperature to 850°C over a frequency range of 5 Hz to 13 MHz. LiTa1-xNbxO3 materials exhibited bulk response with associated capacitances in the order of 10-12 F cm-1 and the temperature-dependent conductivities were found to increase with increasing temperatures. The results showed that LiTa1-xNbxO3 samples were of typical ferroelectrics

Modelling broccoli development, yield and quality

Broccoli is a vegetable crop of increasing importance in Australia, particularly in south-east Queensland and farmers need to maintain a regular supply of good quality broccoli to meet the expanding market. A predictive model of ontogeny, incorporating climatic data including frost risk, would enable farmers to predict harvest maturity date and select appropriate cultivar - sowing date combinations. To develop procedures for predicting ontogeny, yield and quality, field studies using three cultivars, 'Fiesta', 'Greenbelt' and 'Marathon', were sown on eight dates from 11 March to 22 May 1997, and grown under natural and extended (16 h) photoperiods at the University of Queensland, Gatton Campus. Cultivar, rather than the environment, mainly determined head quality attributes of head shape and branching angle. Yield and quality were not influenced by photoperiod. A better understanding of genotype and environmental interactions will help farmers optimise yield and quality, by matching cultivars with time of sowing. The estimated base and optimum temperature for broccoli development were 0 degrees C and 20 degrees C, respectively, and were consistent across cultivars, but thermal time requirements for phenological intervals were cultivar specific. Differences in thermal time requirement from floral initiation to harvest maturity between cultivars were small and of little importance, but differences in thermal time requirement from emergence to floral initiation were large. Sensitivity to photoperiod and solar radiation was low in the three cultivars used. This research has produced models to assist broccoli farmers in crop scheduling and cultivar selection in south-east Queensland

Therapeutic capsule endoscopy: Opportunities and challenges

10.1260/2040-2295.2.4.459Journal of Healthcare Engineering24459-47

Enhanced critical current density in MgB2 superconductor via Si and C coadditions.

In this study, nanosize Silicon and Carbon (Si+C) were reacted with MgB2 in order to enhance the critical current density. The polycrystalline bulks were synthesized by the direct in situ reaction method and their phase formation, crystal structure, and superconducting properties were evaluated. The enhanced relative peak intensity of Mg2Si and MgB4 indicates the formation of a large volume fraction of these two phases with increasing (Si+C) additions. The a-axis lattice parameter shrinks significantly while c-axis increases slightly. The estimated C doping level at B site increases, leading to a degradation of the superconducting transition temperature with increasing (Si+C) additions. By a reaction with (Si+C), the field dependence of critical current density is shown to enhance at both 5 K and 20 K

Simulation of nanodielectrics: nanoparticle and interphase effects on electric field distributions

Nanodielectrics have been regarded as a class of material system that can provide significantly improved chemical, mechanical and dielectric properties over conventional microcomposites. This is due to the presence of a high volume fraction of the interphase between nanoparticles and polymers. However, precise effects of nanodielectrics are not well understood, leading to difficulties in interpreting the dielectric behaviours of nanodielectrics. In the current work, effects of nanoparticle distributions, interparticle distances, nanoparticle sizes, interphase permittivities and interphase thicknesses on the possible electric field variations within a nanodielectric model have been simulated using Finite Element Method Magnetics (FEMM) 4.2. The results demonstrate that different nanoparticle and interphase configurations lead to different effects on the electric field intensity within the nanodielectric model. Mechanisms leading to changes in dielectric properties based on the observed electric field variations are discussed

Variational approximation for mixtures of linear mixed models

Mixtures of linear mixed models (MLMMs) are useful for clustering grouped data and can be estimated by likelihood maximization through the EM algorithm. The conventional approach to determining a suitable number of components is to compare different mixture models using penalized log-likelihood criteria such as BIC.We propose fitting MLMMs with variational methods which can perform parameter estimation and model selection simultaneously. A variational approximation is described where the variational lower bound and parameter updates are in closed form, allowing fast evaluation. A new variational greedy algorithm is developed for model selection and learning of the mixture components. This approach allows an automatic initialization of the algorithm and returns a plausible number of mixture components automatically. In cases of weak identifiability of certain model parameters, we use hierarchical centering to reparametrize the model and show empirically that there is a gain in efficiency by variational algorithms similar to that in MCMC algorithms. Related to this, we prove that the approximate rate of convergence of variational algorithms by Gaussian approximation is equal to that of the corresponding Gibbs sampler which suggests that reparametrizations can lead to improved convergence in variational algorithms as well.Comment: 36 pages, 5 figures, 2 tables, submitted to JCG

Melanocytoma of the optic nerve head: a diagnostic dilemma

The clinical features, autofluorescence, Bscan ultrasonography, optical coherence tomography and fluorescein angiography of the lesion were described. Multiple investigation modalities are needed to confirm the benign nature of the lesion. Careful evaluation and follow-up is crucial to avoid misdiagnosis and erroneous management