Experimental tests of the chiral anomaly magnetoresistance in the Dirac-Weyl semimetals Na3_3Bi and GdPtBi

In the Dirac/Weyl semimetal, the chiral anomaly appears as an "axial" current arising from charge-pumping between the lowest (chiral) Landau levels of the Weyl nodes, when an electric field is applied parallel to a magnetic field $\bf B$. Evidence for the chiral anomaly was obtained from the longitudinal magnetoresistance (LMR) in Na$_3$Bi and GdPtBi. However, current jetting effects (focussing of the current density $\bf J$) have raised general concerns about LMR experiments. Here we implement a litmus test that allows the intrinsic LMR in Na$_3$Bi and GdPtBi to be sharply distinguished from pure current jetting effects (in pure Bi). Current jetting enhances $J$ along the mid-ridge (spine) of the sample while decreasing it at the edge. We measure the distortion by comparing the local voltage drop at the spine (expressed as the resistance $R_{spine}$) with that at the edge ($R_{edge}$). In Bi, $R_{spine}$ sharply increases with $B$ but $R_{edge}$ decreases (jetting effects are dominant). However, in Na$_3$Bi and GdPtBi, both $R_{spine}$ and $R_{edge}$ decrease (jetting effects are subdominant). A numerical simulation allows the jetting distortions to be removed entirely. We find that the intrinsic longitudinal resistivity $\rho_{xx}(B)$ in Na$_3$Bi decreases by a factor of 10.9 between $B$ = 0 and 10 T. A second litmus test is obtained from the parametric plot of the planar angular magnetoresistance. These results strenghthen considerably the evidence for the intrinsic nature of the chiral-anomaly induced LMR. We briefly discuss how the squeeze test may be extended to test ZrTe$_5$.Comment: 17 pages, 8 figures, new co-authors added, new Fig. 6a added. In press, PR

Forecasting product returns for remanufacturing systems

One of the major challenges that a remanufacturer faces at strategic planning level today is to match its supply (returned items) with demand due to the inherited uncertainties and variations on both sides. Forecasting product returns is one of the most important tasks of this matching process. Unlike forecasting for traditional manufacturing systems, both quantity and quality forecasts are critical since return timing, quantity, and the quality of returned products can all vary dramatically. This research develops a forecasting method which incorporates knowledge from related sales, product usage, customer return behavior, and product life expectancy information to provide a more accurate prediction of product returns. The models are validated using Monte Carlo simulations. Numerical cases are also presented to illustrate its usage and some important insights