Development of a Flame Resistant Silicone Rubber

Flame resistant silicone rubber using aluminum silicates and aromatic bromide

Self-consistency of the Excursion Set Approach

The excursion set approach provides a framework for predicting how the abundance of dark matter halos depends on the initial conditions. A key ingredient of this formalism comes from the physics of halo formation: the specification of a critical overdensity threshold (barrier) which protohalos must exceed if they are to form bound virialized halos at a later time. Another ingredient is statistical, as it requires the specification of the appropriate statistical ensemble over which to average when making predictions. The excursion set approach explicitly averages over all initial positions, thus implicitly assuming that the appropriate ensemble is that associated with randomly chosen positions in space, rather than special positions such as peaks of the initial density field. Since halos are known to collapse around special positions, it is not clear that the physical and statistical assumptions which underlie the excursion set approach are self-consistent. We argue that they are at least for low mass halos, and illustrate by comparing our excursion set predictions with numerical data from the DEUS simulations.Comment: 5 pages, 2 figure

Flame resistant elastic elastomeric fiber

Compositions exhibit elastomeric properties and possess various degrees of flame resistance. First material polyurethane, incorporates halogen containing polyol and is flame resistant in air; second contains spandex elastomer with flame retardant additives; and third material is prepared from fluorelastomer composition of copolymer of vinylidene fluoride and hexafluoropropylene

PACS and SPIRE photometer maps of M 33: First results of the HERschel M 33 Extended Survey (HERM33ES)

Context. Within the framework of the HERM33ES key program, we are studying the star forming interstellar medium in the nearby, metal-poor spiral galaxy M 33, exploiting the high resolution and sensitivity of Herschel. Aims. We use PACS and SPIRE maps at 100, 160, 250, 350, and 500 μm wavelength, to study the variation of the spectral energy distributions (SEDs) with galacto-centric distance. Methods. Detailed SED modeling is performed using azimuthally averaged fluxes in elliptical rings of 2 kpc width, out to 8 kpc galacto-centric distance. Simple isothermal and two-component grey body models, with fixed dust emissivity index, are fitted to the SEDs between 24 μm and 500 μm using also MIPS/Spitzer  data, to derive first estimates of the dust physical conditions. Results. The far-infrared and submillimeter maps reveal the branched, knotted spiral structure of M 33. An underlying diffuse disk is seen in all SPIRE maps (250–500 μm). Two component fits to the SEDs agree better than isothermal models with the observed, total and radially averaged flux densities. The two component model, with β fixed at 1.5, best fits the global and the radial SEDs. The cold dust component clearly dominates; the relative mass of the warm component is less than 0.3% for all the fits. The temperature of the warm component is not well constrained and is found to be about 60 K ± 10 K. The temperature of the cold component drops significantly from ~24 K in the inner 2 kpc radius to 13 K beyond 6 kpc radial distance, for the best fitting model. The gas-to-dust ratio for β = 1.5, averaged over the galaxy, is higher than the solar value by a factor of 1.5 and is roughly in agreement with the subsolar metallicity of M 33

A Practical Incremental Learning Framework For Sparse Entity Extraction

This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation criteria, and the overall quality of annotation. We present a framework that integrates Entity Set Expansion (ESE) and Active Learning (AL) to reduce the annotation cost of sparse data and provide an online evaluation method as feedback. This incremental and interactive learning framework allows for rapid annotation and subsequent extraction of sparse data while maintaining high accuracy. We evaluate our framework on three publicly available datasets and show that it drastically reduces the cost of sparse entity annotation by an average of 85% and 45% to reach 0.9 and 1.0 F-Scores respectively. Moreover, the method exhibited robust performance across all datasets.Comment: https://www.aclweb.org/anthology/C18-1059

Non-flammable elastomeric fiber from a fluorinated elastomer and containing an halogenated flame retardant

Flame retardant elastomeric compositions are described comprised of either spandex type polyurethane having incorporated into the polymer chain halogen containing polyols, conventional spandex type polyurethanes in physical admixture with flame retardant additives, or fluoroelastomeric resins in physical admixture with flame retardant additives. Methods are described for preparing fibers of the flame retardant elastomeric materials and articles of manufacture comprised of the flame retardant clastomeric materials and non elastic materials such as polybenzimidazoles, fiberglass, nylons, etc