Analysis of data on retention of high school band students

Includes bibliographical references

TeV radiation from the Crab nebula and other matters

The detection of the Crab Nebula via the Cherenkov imaging technique places TeV astronomy on a secure observational footing. The motivation for TeV observations, a discussion of the atmospheric Cherenkov technique, the experimental details of the Crab Nebula detection, and its scientific implications are presented. The present dilemma of VHE/UHE astronomy is that the Crab appears to be the only source whose showers match theoretical expectations. The situation will be clarified as improved ground-based detectors come on-line with sensitivities matching those of the GRO (Gamma Ray Observatory) instruments

Search for an X-ray identification of a strong gamma-ray source

X-rays from Cygnus X-3 were observed during early 1978 with the detectors of the SAS-3 satellite. These observations in conjunction with earlier UHURU and ANS data indicate that the 4.8 hr period of Cygnus X-3 is increasing at the rate of P/P = (5/1 plus or minus 1.3) x 10 to the minus 6 power/1 yr. The sign and magnitude for this change are incompatible with a rotation model for the period and are in reasonable agreement with model predictions for orbital changes associated with mass loss and transfer in a binary system

X-ray emission from the region of gamma 195+5

X-ray observations of nineteen unidentified discrete celestial gamma ray sources are discussed. Results show some indication of X-rays from the gamma ray source region. On the assumption that the results are valid, it is concluded that the source error box is smaller than what it was

Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model

Vertex centrality measures are a multi-purpose analysis tool, commonly used in many application environments to retrieve information and unveil knowledge from the graphs and network structural properties. However, the algorithms of such metrics are expensive in terms of computational resources when running real-time applications or massive real world networks. Thus, approximation techniques have been developed and used to compute the measures in such scenarios. In this paper, we demonstrate and analyze the use of neural network learning algorithms to tackle such task and compare their performance in terms of solution quality and computation time with other techniques from the literature. Our work offers several contributions. We highlight both the pros and cons of approximating centralities though neural learning. By empirical means and statistics, we then show that the regression model generated with a feedforward neural networks trained by the Levenberg-Marquardt algorithm is not only the best option considering computational resources, but also achieves the best solution quality for relevant applications and large-scale networks. Keywords: Vertex Centrality Measures, Neural Networks, Complex Network Models, Machine Learning, Regression ModelComment: 8 pages, 5 tables, 2 figures, version accepted at IJCNN 2018. arXiv admin note: text overlap with arXiv:1810.1176

GeV Gamma-Ray Sources

We report on the preliminary extension of our work on cataloging the GeV sky to approximately 7 years of CGRO/EGRET observations with special emphasis on a search for transient sources. The search method and significance levels are presented. Our initial results on 13 possible transients indicate that 3 may be new gamma-ray sources. Sixteen new steady GeV sources are also detected, 3 of which have never been reported as gamma-ray sources