Detection of Minimum-Ionizing Particles and Nuclear Counter Effect with Pure BGO and BSO Crystals with Photodiode Read-out

Long BGO (Bismuth Germanate) and BSO (Bismuth Silicate) crystals coupled with silicon photodiodes have been used to detect minimum-ionizing particles(MIP). With a low noise amplifier customized for this purpose, the crystals can detect MIPs with an excellent signal-to-noise ratio. The NCE(Nuclear Counter Effect} is also clearly observed and measured. Effect of full and partial wrapping of a reflector around the crystal on light collection is also studied.Comment: 18 pages, including 5 figures; LaTeX and EP

Radar RFI at Goldstone DSS 12 and DSS 16

Radio frequency interference (RFI) from the DSS 14 Goldstone Solar System Radar (GSSR) was investigated at DSS 12 and DSS 16 with the goal of assisting in the choice of the location of future DSN antennas. Total power measurements at both locations were made at the S-band carrier frequency of 2320 MHz. X-band measurements at the carrier frequency of 8495 MHz could not be made. Exciter-chain output spectrum and klystron output spectrum measurements were made at S- and X-bands using a probable worst-case modulation of the radar signal (short pseudorandom number (PN) code length and short pulse length). Based on these measurements, it is estimated that RFI levels in the DSN receiving bands at both sites (above 10-deg elevation) would be below -192 dBm for a 1-Hz bandwidt

Fluctuation Analysis of Human Electroencephalogram

The scaling behaviors of the human electroencephalogram (EEG) time series are studied using detrended fluctuation analysis. Two scaling regions are found in nearly every channel for all subjects examined. The scatter plot of the scaling exponents for all channels (up to 129) reveals the complicated structure of a subject's brain activity. Moment analyses are performed to extract the gross features of all the scaling exponents, and another universal scaling behavior is identified. A one-parameter description is found to characterize the fluctuation properties of the nonlinear behaviors of the brain dynamics.Comment: 4 pages in RevTeX + 6 figures in ep

Multiple G-It\^{o} integral in the G-expectation space

In this paper, motivated by mathematic finance we introduce the multiple G-It\^{o} integral in the G-expectation space, then investigate how to calculate. We get the the relationship between Hermite polynomials and multiple G-It\^{o} integrals which is a natural extension of the classical result obtained by It\^{o} in 1951.Comment: 9 page

Long-term power-law fluctuation in Internet traffic

Power-law fluctuation in observed Internet packet flow are discussed. The data is obtained by a multi router traffic grapher (MRTG) system for 9 months. The internet packet flow is analyzed using the detrended fluctuation analysis. By extracting the average daily trend, the data shows clear power-law fluctuations. The exponents of the fluctuation for the incoming and outgoing flow are almost unity. Internet traffic can be understood as a daily periodic flow with power-law fluctuations.Comment: 10 pages, 8 figure

Probing High Redshift Radiation Fields with Gamma-Ray Absorption

The next generation of gamma-ray telescopes may be able to observe gamma-ray blazars at high redshift, possibly out to the epoch of reionization. The spectrum of such sources should exhibit an absorption edge due to pair-production against UV photons along the line of sight. One expects a sharp drop in the number density of UV photons at the Lyman edge E_{L}. This implies that the universe becomes transparent after gamma-ray photons redshift below E (m_{e}c^2)^{2}/E_{L} 18 GeV. Thus, there is only a limited redshift interval over which GeV photons can pair produce. This implies that any observed absorption will probe radiation fields in the very early universe, regardless of the subsequent star formation history of the universe. Furthermore, measurements of differential absorption between blazars at different redshifts can cleanly isolate the opacity due to UV emissivity at high redshift. An observable absorption edge should be present for most reasonable radiation fields with sufficient energy to reionize the universe. Ly-alpha photons may provide an important component of the pair-production opacity. Observations of a number of blazars at different redshifts will thus allow us to probe the rise in comoving UV emissivity with time.Comment: ApJ accepted version, minor changes. 19 pages, 5 figure

Formation and kinetics of transient metastable states in mixtures under coupled phase ordering and chemical demixing

We present theory and simulation of simultaneous chemical demixing and phase ordering in a polymer-liquid crystal mixture in conditions where isotropic- isotropic phase separation is metastable with respect to isotropic-nematic phase transition. It is found that mesophase formation proceeds by a transient metastable phase that surround the ordered phase, and whose lifetime is a function of the ratio of diffusional to orientational mobilities. It is shown that kinetic phase ordering in polymer-mesogen mixtures is analogous to kinetic crystallization in polymer solutions.Comment: 17 pages, 5 figures accepted for publication in EP

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