23,275 research outputs found
Multi chiral-doublets in one single nucleus
Adiabatic and configuration-fixed constraint triaxial relativistic mean field
(RMF) approaches are developed for the first time and a new phenomenon, the
existence of multi chiral-doublets (MD), i.e., more than one pairs of
chiral doublets bands in one single nucleus, is suggested for nuclei in A~100
region, typically for Rh, based on the triaxial deformations together
with their corresponding proton and neutron configurations.Comment: 10 pages, 4 figure
Cooperative and Distributed Localization for Wireless Sensor Networks in Multipath Environments
We consider the problem of sensor localization in a wireless network in a
multipath environment, where time and angle of arrival information are
available at each sensor. We propose a distributed algorithm based on belief
propagation, which allows sensors to cooperatively self-localize with respect
to one single anchor in a multihop network. The algorithm has low overhead and
is scalable. Simulations show that although the network is loopy, the proposed
algorithm converges, and achieves good localization accuracy
Progress on tilted axis cranking covariant density functional theory for nuclear magnetic and antimagnetic rotation
Magnetic rotation and antimagnetic rotation are exotic rotational phenomena
observed in weakly deformed or near-spherical nuclei, which are
respectivelyinterpreted in terms of the shears mecha-nism and two shearslike
mechanism. Since their observations, magnetic rotation and antimagnetic
rotation phenomena have been mainly investigated in the framework of tilted
axis cranking based on the pairing plus quadrupole model. For the last decades,
the covariant density functional theory and its extension have been proved to
be successful in describing series of nuclear ground-states and excited states
properties, including the binding energies, radii, single-particle spectra,
resonance states, halo phenomena, magnetic moments, magnetic rotation,
low-lying excitations, shape phase transitions, collective rotation and
vibrations, etc. This review will mainly focus on the tilted axis cranking
covariant density functional theory and its application for the magnetic
rotation and antimagnetic rotation phenomena.Comment: 53 pages, 19 figure
Distributed Local Linear Parameter Estimation using Gaussian SPAWN
We consider the problem of estimating local sensor parameters, where the
local parameters and sensor observations are related through linear stochastic
models. Sensors exchange messages and cooperate with each other to estimate
their own local parameters iteratively. We study the Gaussian Sum-Product
Algorithm over a Wireless Network (gSPAWN) procedure, which is based on belief
propagation, but uses fixed size broadcast messages at each sensor instead.
Compared with the popular diffusion strategies for performing network parameter
estimation, whose communication cost at each sensor increases with increasing
network density, the gSPAWN algorithm allows sensors to broadcast a message
whose size does not depend on the network size or density, making it more
suitable for applications in wireless sensor networks. We show that the gSPAWN
algorithm converges in mean and has mean-square stability under some technical
sufficient conditions, and we describe an application of the gSPAWN algorithm
to a network localization problem in non-line-of-sight environments. Numerical
results suggest that gSPAWN converges much faster in general than the diffusion
method, and has lower communication costs, with comparable root mean square
errors
Hawking radiation, W-infinity algebra and trace anomalies
We apply the "trace anomaly method" to the calculation of moments of the
Hawking radiation of a Schwarzschild black hole. We show that they can be
explained as the fluxes of chiral currents forming a W-infinity algebra. Then
we construct the covariant version of these currents and verify that up to
order 6 they are not affected by any trace anomaly. Using cohomological methods
we show that actually, for the fourth order current, no trace anomalies can
exist. The results reported here are strictly valid in two dimensions.Comment: 22 pages, typos correcte
Stabilization of molten salt materials using metal chlorides for solar thermal storage
The effect of a variety of metal-chlorides additions on the melting behavior and thermal stability of commercially available salts was investigated. Ternary salts comprised of KNO3, NaNO2, and NaNO3 were produced with additions of a variety of chlorides (KCl, LiCl, CaCl2, ZnCl2, NaCl and MgCl2). Thermogravimetric analysis and weight loss experiments showed that the quaternary salt containing a 5 wt% addition of LiCl and KCl led to an increase in short term thermal stability compared to the ternary control salts. These additions allowed the salts to remain stable up to a temperature of 630 °C. Long term weight loss experiments showed an upper stability increase of 50 °C. A 5 wt% LiCl addition resulted in a weight loss of only 25% after 30 hours in comparison to a 61% loss for control ternary salts. Calorimetry showed that LiCl additions allow partial melting at 80 °C, in comparison to the 142 °C of ternary salts. This drop in melting point, combined with increased stability, provided a molten working range increase of almost 100 °C in total, in comparison to the control ternary salts. XRD analysis showed the oxidation effect of decomposing salts and the additional phase created with LiCl additions to allow melting point changes to occur
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Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks
Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. Our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product
Hawking radiation from the Schwarzschild black hole with a global monopole via gravitational anomaly
Hawking flux from the Schwarzschild black hole with a global monopole is
obtained by using Robinson and Wilczek's method. Adopting a dimension reduction
technique, the effective quantum field in the (3+1)--dimensional global
monopole background can be described by an infinite collection of the
(1+1)--dimensional massless fields if neglecting the ingoing modes near the
horizon, where the gravitational anomaly can be cancelled by the
(1+1)--dimensional black body radiation at the Hawking temperature.Comment: 4 pages, no figure, 3nd revsion with one reference adde
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