Mass flow through solid 4He induced by the fountain effect

Using an apparatus that allows superfluid liquid 4He to be in contact with hcp solid \4he at pressures greater than the bulk melting pressure of the solid, we have performed experiments that show evidence for 4He mass flux through the solid and the likely presence of superfluid inside the solid. We present results that show that a thermomechanical equilibrium in quantitative agreement with the fountain effect exists between two liquid reservoirs connected to each other through two superfluid-filled Vycor rods in series with a chamber filled with solid 4He. We use the thermomechanical effect to induce flow through the solid and measure the flow rate. On cooling, mass flux appears near T = 600 mK and rises smoothly as the temperature is lowered. Near T = 75 mK a sharp drop in the flux is present. The flux increases as the temperature is reduced below 75 mK. We comment on possible causes of this flux minimum.Comment: 20 pages, 22 figures, 7 table

Petawatt laser absorption bounded

The interaction of petawatt ($10^{15}\ \mathrm{W}$) lasers with solid matter forms the basis for advanced scientific applications such as table-top particle accelerators, ultrafast imaging systems and laser fusion. Key metrics for these applications relate to absorption, yet conditions in this regime are so nonlinear that it is often impossible to know the fraction of absorbed light $f$, and even the range of $f$ is unknown. Here using a relativistic Rankine-Hugoniot-like analysis, we show for the first time that $f$ exhibits a theoretical maximum and minimum. These bounds constrain nonlinear absorption mechanisms across the petawatt regime, forbidding high absorption values at low laser power and low absorption values at high laser power. For applications needing to circumvent the absorption bounds, these results will accelerate a shift from solid targets, towards structured and multilayer targets, and lead the development of new materials

Absence of long-range chemical ordering in equimolar FeCoCrNi

Equimolar FeCoCrNi alloys have been the topic of recent research as "high-entropy alloys," where the name is derived from the high configurational entropy of mixing for a random solid solution. Despite their name, no systematic study of ordering in this alloy system has been performed to date. Here, we present results from anomalous x-ray scattering and neutron scattering on quenched and annealed samples. An alloy of FeNi_3 was prepared in the same manner to act as a control. Evidence of long-range chemical ordering is clearly observed in the annealed FeNi_3 sample from both experimental techniques. The FeCoCrNi sample given the same heat treatment lacks long-range chemical order

Multilayered feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered networks have been constructed with sigmoid non-linearity. The models under study are different in the number of hidden neurons. After a thorough training and test procedure, neural net with three nodes in the hidden layer is found to be the best predictive model.Comment: 19 pages, 1 table, 3 figure

Towards Designing an Integrated Architecture for NEO Characterization, Mitigation, Scientific Evaluation, and Resource Utilization

This poster reviews the planning and design for an integrated architecture for characterization, mitigation, scientific evaluation and resource utilization of near earth objects. This includes tracks to observe and characterize the nature of the threat posed by a NEO, and deflect if a significant threat is posed. The observation stack can also be used for a more complete scientific analysis of the NEO

Artificial Neural Network to predict mean monthly total ozone in Arosa, Switzerland

Present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Networks models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with learning rate 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found skillful. But, Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.Comment: 22 pages, 14 figure

Ultrafast demagnetization of Co 25Ni 75/Pt multilayers with perpendicular anisotropy at elevated temperatures

Copyright © 2005 American Institute of PhysicsUltrafast demagnetization has been studied in Si/Pt(160 Å)/[Co25Ni75(x)/Pt(8 Å)]20 (x = 3, 4.5, and 6 Å) multilayers with perpendicular anisotropy by magneto-optical pump-probe measurements in the polar geometry. Time-resolved measurements made in the saturated state showed that maximum demagnetization was achieved within 300 fs. Hysteresis loops were measured at a time delay of 1.3 ps for temperatures from 20 to 300 °C. The Curie temperature was found to increase from 150 to 250 °C with increasing Co25Ni75 thickness. By comparing the loops obtained with and without pump excitation, the increase in electron temperature due to the pump was estimated to be about 60 K