The effect of carbon encapsulation on the magnetic properties of Ni nanoparticles produced by arc discharge in de-ionized water

Despite intensive research on optimizing the methods for depositing carbon encapsulated ferromagnetic nanoparticles, the effect of the carbon cages remains unclear. In the present work, the effect of the graphitic cages on the magnetization of the ferromagnetic core has been studied by comparing the magnetic properties of pure and carbon encapsulated Ni particles of the same size. The carbon encapsulated Ni particles were formed using an electric arc discharge in de-ionized water between a solid graphite cathode and an anode consisting of Ni and C in a mass ratio of Ni:C = 7:3. This method is shown to have potential for low cost production of carbon encapsulated Ni nanoparticle samples with narrow particle size distributions. X-ray diffraction (XRD) and high resolution transmission electron microscopy (HRTEM) analysis were used to study the crystallography, morphology, and size distribution of the encapsulated and pure Ni nanoparticle samples. The availability of encapsulated particles with various sizes allowed us to elucidate the role of carbon cages in size-dependent properties. Our data suggest that even though encapsulation is beneficial for protection against hostile chemical environments and for avoiding low proximity phenomena, it suppresses the saturation magnetization of the Ni cores.

Design, construction and evaluation of systems to predict risk in obstetrics.

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

EARLY FLOWERING4 recruitment of EARLY FLOWERING3 in the nucleus sustains the Arabidopsis circadian clock

The plant circadian clock is proposed to be a network of several interconnected feedback loops, and loss of any component leads to changes in oscillator speed. We previously reported that Arabidopsis thaliana EARLY FLOWERING4 (ELF4) is required to sustain this oscillator and that the elf4 mutant is arrhythmic. This phenotype is shared with both elf3 and lux. Here, we show that overexpression of either ELF3 or LUX ARRHYTHMO (LUX) complements the elf4 mutant phenotype. Furthermore, ELF4 causes ELF3 to form foci in the nucleus. We used expression data to direct a mathematical position of ELF3 in the clock network. This revealed direct effects on the morning clock gene PRR9, and we determined association of ELF3 to a conserved region of the PRR9 promoter. A cis-element in this region was suggestive of ELF3 recruitment by the transcription factor LUX, consistent with both ELF3 and LUX acting genetically downstream of ELF4. Taken together, using integrated approaches, we identified ELF4/ELF3 together with LUX to be pivotal for sustenance of plant circadian rhythms. © 2012 American Society of Plant Biologists.

Implementation of multi-group cross-section methodology in BGCore MC-depletion code

BGCore is a software package for comprehensive computer simulation of nuclear reactor systems and their fuel cycles. The BGCore interfaces Monte Carlo particles transport code MCNP4C with a SARAF module - an independently developed code for calculating in-core fuel composition and spent fuel emissions following discharge. In BGCore system, depletion coupling methodology is based on the multi-group approach that significantly reduces computation time and allows tracking of large number of nuclides during calculations. In this study, burnup calculation capabilities of BGCore system were validated against well established and verified, computer codes for thermal and fast spectrum lattices. Very good agreement in k eigenvalue and nuclide densities prediction was observed for all cases under consideration. In addition, decay heat prediction capabilities of the BGCore system were benchmarked against the most recent edition of ANS Standard methodology for UO2 fuel decay power prediction in LWRs. It was found that the difference between ANS standard data and that predicted by the BGCore does not exceed 5%.