Site specific spin dynamics in BaFe2As2: tuning the ground state by orbital differentiation

The role of orbital differentiation on the emergence of superconductivity in the Fe-based superconductors remains an open question to the scientific community. In this investigation, we employ a suitable microscopic spin probe technique, namely Electron Spin Resonance (ESR), to investigate this issue on selected chemically substituted BaFe$_{2}$As$_{2}$ single crystals. As the spin-density wave (SDW) phase is suppressed, we observe a clear increase of the Fe 3$d$ bands anisotropy along with their localization at the FeAs plane. Such an increase of the planar orbital content interestingly occurs independently on the chemical substitution responsible for suppressing the SDW phase. As a consequence, the magnetic fluctuations combined with the resultant particular symmetry of the Fe 3$d$ bands are propitious ingredients to the emergence of superconductivity in this class of materials.Comment: 6 pages, 5 figure

X-ray photoelectron spectroscopy measurement of valence-band offsets for Mg-based semiconductor compounds

We have used x-ray photoelectron spectroscopy to measure the valence-band offsets for the lattice matched MgSe/Cd0.54Zn0.46Se and MgTe/Cd0.88Zn0.12Te heterojunctions grown by molecular beam epitaxy. By measuring core level to valence-band maxima and core level to core level binding energy separations, we obtain values of 0.56+/-0.07 eV and 0.43+/-0.11 eV for the valence-band offsets of MgSe/Cd0.54Zn0.46Se and MgTe/Cd0.88Zn0.12Te, respectively. Both of these values deviate from the common anion rule, as may be expected given the unoccupied cation d orbitals in Mg. Application of our results to the design of current II-VI wide band-gap light emitters is discussed

Assessment of the learning curve in health technologies: a systematic review

Objective: We reviewed and appraised the methods by which the issue of the learning curve has been addressed during health technology assessment in the past. Method: We performed a systematic review of papers in clinical databases (BIOSIS, CINAHL, Cochrane Library, EMBASE, HealthSTAR, MEDLINE, Science Citation Index, and Social Science Citation Index) using the search term "learning curve:" Results: The clinical search retrieved 4,571 abstracts for assessment, of which 559 (12%) published articles were eligible for review. Of these, 272 were judged to have formally assessed a learning curve. The procedures assessed were minimal access (51%), other surgical (41%), and diagnostic (8%). The majority of the studies were case series (95%). Some 47% of studies addressed only individual operator performance and 52% addressed institutional performance. The data were collected prospectively in 40%, retrospectively in 26%, and the method was unclear for 31%. The statistical methods used were simple graphs (44%), splitting the data chronologically and performing a t test or chi-squared test (60%), curve fitting (12%), and other model fitting (5%). Conclusions: Learning curves are rarely considered formally in health technology assessment. Where they are, the reporting of the studies and the statistical methods used are weak. As a minimum, reporting of learning should include the number and experience of the operators and a detailed description of data collection. Improved statistical methods would enhance the assessment of health technologies that require learning

Assessing the learning curve effect in health technologies: Lessons from the non-clinical literature

Introduction: Many health technologies exhibit some form of learning effect, and this represents a barrier to rigorous assessment. It has been shown that the statistical methods used are relatively crude. Methods to describe learning curves in fields outside medicine, for example, psychology and engineering, may be better. Methods: To systematically search non–health technology assessment literature (for example, PsycLit and Econlit databases) to identify novel statistical techniques applied to learning curves. Results: The search retrieved 9,431 abstracts for assessment, of which 18 used a statistical technique for analyzing learning effects that had not previously been identified in the clinical literature. The newly identified methods were combined with those previously used in health technology assessment, and categorized into four groups of increasing complexity: a) exploratory data analysis; b) simple data analysis; c) complex data analysis; and d) generic methods. All the complex structured data techniques for analyzing learning effects were identified in the nonclinical literature, and these emphasized the importance of estimating intra- and interindividual learning effects. Conclusion: A good dividend of more sophisticated methods was obtained by searching in nonclinical fields. These methods now require formal testing on health technology data sets

Characterization of the residual stresses in spray-formed steels using neutron diffraction

Neutron diffraction was used to characterize the residual stresses in an as-sprayed tube-shaped steel preform. The measured residual stress distributions were compared with those simulated using finite element method by taking into account the effects of the thermal history, porosity and different phases of the sprayed preform. The porosity was measured using X-ray microcomputed tomography. The study revealed for the first time the correlation between the distribution of porosity and residual stress developed in the as-sprayed preform

Aircraft observations and model simulations of concentration and particle size distribution in the Eyjafjallajökull volcanic ash cloud

The Eyjafjallajökull volcano in Iceland emitted a cloud of ash into the atmosphere during April and May 2010. Over the UK the ash cloud was observed by the FAAM BAe-146 Atmospheric Research Aircraft which was equipped with in-situ probes measuring the concentration of volcanic ash carried by particles of varying sizes. The UK Met Office Numerical Atmospheric-dispersion Modelling Environment (NAME) has been used to simulate the evolution of the ash cloud emitted by the Eyjafjallajökull volcano during the period 4–18 May 2010. In the NAME simulations the processes controlling the evolution of the concentration and particle size distribution include sedimentation and deposition of particles, horizontal dispersion and vertical wind shear. For travel times between 24 and 72 h, a 1/t relationship describes the evolution of the concentration at the centre of the ash cloud and the particle size distribution remains fairly constant. Although NAME does not represent the effects of microphysical processes, it can capture the observed decrease in concentration with travel time in this period. This suggests that, for this eruption, microphysical processes play a small role in determining the evolution of the distal ash cloud. Quantitative comparison with observations shows that NAME can simulate the observed column-integrated mass if around 4% of the total emitted mass is assumed to be transported as far as the UK by small particles (< 30 μm diameter). NAME can also simulate the observed particle size distribution if a distal particle size distribution that contains a large fraction of < 10 μm diameter particles is used, consistent with the idea that phraetomagmatic volcanoes, such as Eyjafjallajökull, emit very fine particles