Fabrication of binder-free ultrafine WC-6CO composites by coupled multi-physical fields activation technology

A novel sintering method, named as coupled multi-physical fields activation technology, has been introduced for the forming of various material powder systems. Compared with the conventional ones, this technique presents more advantages: lower sintering temperature, shorter forming time, and remarkable inhibition of the grains coarsening. In the study, the cylinders of Φ4.0mm×4.0mm had been formed with ultrafine WC-6Co powders. The relative properties of sintered WC-6Co cemented carbides, such as hardness and the microstructures, had been obtained. The study has shown that a relative density, 97.80%, of the formed samples, could been achieved when the case of temperature 850℃, heating rate 50℃/s, pressure 75MPa and Electro-heating loop 6 times, were used. More importantly, the circumscription for the growth of grain size of WC, attributed to the effect of electrical field, renders coupled multi-physical fields activation technology applicable for getting WC-6Co cemented carbides with fine grain size and good properties

Anomalous Hall effect in heavy electron materials

We propose a new empirical formula for the anomalous Hall effect in heavy electron materials based on a phenomenological two-fluid description of the f-electron states. The new formula incorporates two previous theories proposed by Fert and Levy in 1987 and Kontani et al. in the early 1990s and takes into account both incoherent and coherent skew scatterings from local and itinerant f-electrons. We perform experimental analysis in several heavy electron compounds and show that the new formula provides a consistent description of the evolution of the Hall coefficient in the whole temperature range down to only a few Kelvin.Comment: 6 pages, 4 figure

Sparsity Oriented Importance Learning for High-dimensional Linear Regression

With now well-recognized non-negligible model selection uncertainty, data analysts should no longer be satisfied with the output of a single final model from a model selection process, regardless of its sophistication. To improve reliability and reproducibility in model choice, one constructive approach is to make good use of a sound variable importance measure. Although interesting importance measures are available and increasingly used in data analysis, little theoretical justification has been done. In this paper, we propose a new variable importance measure, sparsity oriented importance learning (SOIL), for high-dimensional regression from a sparse linear modeling perspective by taking into account the variable selection uncertainty via the use of a sensible model weighting. The SOIL method is theoretically shown to have the inclusion/exclusion property: When the model weights are properly around the true model, the SOIL importance can well separate the variables in the true model from the rest. In particular, even if the signal is weak, SOIL rarely gives variables not in the true model significantly higher important values than those in the true model. Extensive simulations in several illustrative settings and real data examples with guided simulations show desirable properties of the SOIL importance in contrast to other importance measures