Equivariant KK-theory of GKM bundles

Given a fiber bundle of GKM spaces, $\pi\colon M\to B$, we analyze the structure of the equivariant $K$-ring of $M$ as a module over the equivariant $K$-ring of $B$ by translating the fiber bundle, $\pi$, into a fiber bundle of GKM graphs and constructing, by combinatorial techniques, a basis of this module consisting of $K$-classes which are invariant under the natural holonomy action on the $K$-ring of $M$ of the fundamental group of the GKM graph of $B$. We also discuss the implications of this result for fiber bundles $\pi\colon M\to B$ where $M$ and $B$ are generalized partial flag varieties and show how our GKM description of the equivariant $K$-ring of a homogeneous GKM space is related to the Kostant-Kumar description of this ring.Comment: 15 page

Studies Regarding the Increase in Hydrophilicity of Some Textile Fabrics through Treatment by Cold Plasma and Grafting with Vinyl Monomers

The polyamide and polyester fabrics have been treated by glow discharge plasma and grafted with 2- hydroxyethylmethacrylate (HEMA) in order to increase the hydrophilicity and to improve the soil release properties. The plasma treatments were carried out at different exposure times. Grafting was obtained with gaseous HEMA and with alcohol or aqueous solution of HEMA at different concentrations. The physical and chemical surface changes were studied using scanning electron microscopy, infrared spectroscopy, colour measurements. The hydrophilicity was assessed through the absorption time of a water drop and the soil removal through washing by remission values

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants