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 (M$\chi$D), i.e., more than one pairs of chiral doublets bands in one single nucleus, is suggested for nuclei in A~100 region, typically for $^{106}$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

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 Fluxes, Back reaction and Covariant Anomalies

Starting from the chiral covariant effective action approach of Banerjee and Kulkarni [Phys. Lett. B 659, 827(2008)], we provide a derivation of the Hawking radiation from a charged black hole in the presence of gravitational back reaction. The modified expressions for charge and energy flux, due to effect of one loop back reaction are obtained.Comment: 6 pages, no figures, minor changes and references added, to appear in Classical and Quantum Gravit