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We investigate the potential of neural-network based classifiers for discriminating gravitational wave bursts (GWBs) of a given canonical family (e.g. core-collapse supernova waveforms) from typical transient instrumental artifacts (glitches), in the data of a single detector. The further classification of glitches into typical sets is explored. In order to provide a proof of concept, we use the core-collapse supernova waveform catalog produced by H. Dimmelmeier and co-Workers, and the data base of glitches observed in laser interferometer gravitational wave observatory (LIGO) data maintained by P. Saulson and co-Workers to construct datasets of (windowed) transient waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian) noise with different signal-to-noise ratios (SNR). Principal component analysis (PCA) is next implemented for reducing data dimensionality, yielding results consistent with, and extending those in the literature. Then, a multilayer perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset, and used to classify the transients as glitch or burst. A Self-Organizing Map (SOM) architecture is finally used to classify the glitches. The glitch/burst discrimination and glitch classification abilities are gauged in terms of the related truth tables. Preliminary results suggest that the approach is effective and robust throughout the SNR range of practical interest. Perspective applications pertain both to distributed (network, multisensor) detection of GWBs, where some intelligence at the single node level can be introduced, and instrument diagnostics/optimization, where spurious transients can be identified, classified and hopefully traced back to their entry points.

It has been hypothesized recently that core collapse supernovae are triggered by mildly relativistic jets following observations of radio properties of these explosions. Association of a jet, similar to a gamma-ray burst jet but only slower, allows shock acceleration of particles to high energy and non-thermal neutrino emission from a supernova. Detection of these high energy neutrinos in upcoming kilometer scale Cherenkov detectors may be the only direct way to probe inside these astrophysical phenomena as electromagnetic radiation is thermal and contains little information. Calculation of high energy neutrino signal from a simple and slow jet model buried inside the pre-supernova star is reviewed here. The detection prospect of these neutrinos in water or ice detector is also discussed in this brief review. Jetted core collapse supernovae in nearby galaxies may provide the strongest high energy neutrino signal from point sources.

Oscillatory patterns in a one-pool model with Ca²⁺-activated IP₃ degradation are presented and classified, based on fast/slow dynamical analysis and two-parameter bifurcations. The whole parameter space can be partitioned into two parts, that is, the oscillatory and rest regions. For this purpose, we perform the continuation of fold bifurcation, Hopf bifurcation and saddle-node bifurcation of limit cycles, respectively. Several distinct topological types of complex intracellular Ca²⁺ oscillations (especially bursting) are investigated in the oscillatory area. The classification and transition of different types of Ca²⁺ oscillations may offer a possible explanation for the differences in dynamic behavior observed in real cells in response to different levels of stimulation.

A burst is an unusually large number of events occurring within a certain period of time. Various burst analysis methods over multiple window size have been proposed. However, real-time burst analysis is difficult owing to the huge amount of computation. This article proposes a method to analyze a burst in real-time. The proposed method reduces computation by avoiding redundant data updates of no event occurrence, and by suppressing data within a certain period in the case of emergent increase of events. In addition, a level and a rank of a burst can be analyzed simultaneously. The effectiveness of the proposed method is evaluated by experiments with real data. The result shows that the proposed method is more effective in analyzing real data, and more efficient in handling a large amount of events.

Dragon-King^a is introduced in this paper as the manifestation of a disaster incident for a Smart Grid’s blackout for a day-ahead. The quintessential incident is being generated by an artificial black hole, it then spreads quickly into transmission lines. Consequently, it bursts into space-time transition regions. This paper reveals all the essential characteristics of the Dragon-King, it has non-isotropic stochastic kinematics. Regardless of its huge manifestation of unwanted phenomena, the Dragon-King is controllable. We also describe the mechanism of an updated complexity, spinning cosmic string and a messenger system in this paper. Furthermore, the control strategy of the feedback controller which ultimately leads to a stable normal operation is also reported in this paper.