Temperature-sensed cryogenic bleed maintains liquid state in transfer line

Inverted tee, installed at a high point in a cryogenic transfer line, is equipped with an insulated bleed line that passes a fixed amount of cryogenic fluid at atmospheric pressure. A sensing device activates a vent valve in the tee stack whenever gaseous nitrogen is present

Inexpensive insulation is effective for cryogenic transfer lines

Matting cover thermally insulates cryogenic-liquid transfer pipelines. The matting consists of layers of commercially available fiber glass tape in which the fibers are randomly oriented in parallel planes

Nonmetallic impurities improve mechanical properties of vapor-deposited tungsten

Mechanical properties of vapor deposited tungsten are improved by selective incorporation of various nonmetallic impurities. Addition of trace quantities of carbon, nitrogen, or oxygen can significantly increase both low and high temperature yield strength without greatly affecting ductile-to-brittle transition temperature

Vapor Deposited Tungsten for Application as a Thermionic Emitter Material

Purity and resistance to grain growth of vapor deposited tungsten tubing for use as thermionic emitte

Effects of additions of nonmetallics on the properties of vapor-deposited tungsten

Nonmetallic additive effects on properties of vapor deposited tungste

Tungsten cladding of reactor fuels

Tungsten cladding of reactor fuel

Estimating the Expected Value of Partial Perfect Information in Health Economic Evaluations using Integrated Nested Laplace Approximation

The Expected Value of Perfect Partial Information (EVPPI) is a decision-theoretic measure of the "cost" of parametric uncertainty in decision making used principally in health economic decision making. Despite this decision-theoretic grounding, the uptake of EVPPI calculations in practice has been slow. This is in part due to the prohibitive computational time required to estimate the EVPPI via Monte Carlo simulations. However, recent developments have demonstrated that the EVPPI can be estimated by non-parametric regression methods, which have significantly decreased the computation time required to approximate the EVPPI. Under certain circumstances, high-dimensional Gaussian Process regression is suggested, but this can still be prohibitively expensive. Applying fast computation methods developed in spatial statistics using Integrated Nested Laplace Approximations (INLA) and projecting from a high-dimensional into a low-dimensional input space allows us to decrease the computation time for fitting these high-dimensional Gaussian Processes, often substantially. We demonstrate that the EVPPI calculated using our method for Gaussian Process regression is in line with the standard Gaussian Process regression method and that despite the apparent methodological complexity of this new method, R functions are available in the package BCEA to implement it simply and efficiently

Embodiment and designing learning environments

There is increasing recognition amongst learning sciences researchers of the critical role that the body plays in thinking and reasoning across contexts and across disciplines. This workshop brings ideas of embodied learning and embodied cognition to the design of instructional environments that engage learners in new ways of moving within, and acting upon, the physical world. Using data and artifacts from participants' research and designs as a starting point, this workshop focuses on strategies for how to effectively leverage embodiment in learning activities in both technology and non-technology environments. Methodologies for studying/assessing the body's role in learning are also addressed

Coupled-cluster calculations of properties of Boron atom as a monovalent system

We present relativistic coupled-cluster (CC) calculations of energies, magnetic-dipole hyperfine constants, and electric-dipole transition amplitudes for low-lying states of atomic boron. The trivalent boron atom is computationally treated as a monovalent system. We explore performance of the CC method at various approximations. Our most complete treatment involves singles, doubles and the leading valence triples. The calculations are done using several approximations in the coupled-cluster (CC) method. The results are within 0.2-0.4% of the energy benchmarks. The hyperfine constants are reproduced with 1-2% accuracy