SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.     

TRIFFID observations of the cores of the three globular clusters M15, M92 and NGC 6712

We present the results of TRIFFID simultaneous V - and B -band observations of the cores of the globular clusters M15, M92 and NGC 6712. A variability search of their dense centres was made feasible through performing post-exposure image sharpening on the images, increasing the image resolution by a factor of ∼2. The isis implementation of the image subtraction technique developed by Alard & Lupton was then used to detect flux variations in our image sets. We have obtained periods for all observable variables (in our field of view) in NGC 6712 and we have found two new RR Lyrae variables (an RRab and an RRc). We have confirmed three variables in our field of view of the M92. For M15, we detect 48 variables in our field of view, 23 of which are new discoveries. We obtain periods and amplitudes for all variables and classify new ones based on the light-curve shape, the most significant period and the mean magnitude in the V band. Among the detected RR Lyrae we find 19 RRc, 12 RRab and two RRd types. In the subsequent analysis we find a marked increase in RRc over RRab variables in the core. In a refined procedure to search for fainter objects we find no dwarf novae in our field of view of M15. Simulations performed on the data set to quantify our sensitivity to such objects indicate that an upper limit of 10 dwarf novae (at 92 per cent probability) exist in our field of view. The implications this result has on globular clusters are discussed.     

WR 7a: a V Sagittae or a qWR star?

The star WR 7a, also known as SPH 2, has a spectrum that resembles that of V Sagittae stars although no O  vi emission has been reported. The Temporal Variance Spectrum – TVS – analysis of our data shows weak but strongly variable emission of O  vi lines which is below the noise level in the intensity spectrum.
Contrary to what is seen in V Sagittae stars, optical photometric monitoring shows very little, if any, flickering. We found evidence of periodic variability. The most likely photometric period is   P _phot= 0.227(±14) d  , while radial velocities suggest a period of   P _spec= 0.204(±13) d  . One-day aliases of these periods can not be ruled out. We call attention to similarities with HD 45166 and DI Cru (= WR 46), where multiple periods are present. They may be associated to the binary motion or to non-radial oscillations.
In contrast to a previous conclusion by Pereira et al., we show that WR 7a contains hydrogen. The spectrum of the primary star seems to be detectable as the N  v 4604 Å  absorption line is visible. If so, it means that the wind is optically thin in the continuum and that it is likely to be a helium main sequence star.
Given the similarity to HD 45166, we suggests that WR 7a may be a qWR – quasi Wolf–Rayet – star. Its classification is WN4h/CE in the Smith, Shara & Moffat three-dimensional classification system.